TATA Motors Stock Prediction

Objective: -

Predicting the stock market has been the bane and goal of investors since its inception. Every day billions of dollars are traded on the stock exchange, and behind every dollar is an investor hoping to make a profit in one way or another. Entire companies rise and fall daily depending on market behaviour. If an investor is able to accurately predict market movements, he offers a tantalizing promise of wealth and influence.

A stock market is a public market where you can buy and sell shares for publicly listed companies. The stocks, also known as equities, represent ownership in the company. The stock exchange is the mediator that allows the buying and selling of shares.

Recently, we have seen an increase of more than 10 per cent in the stock price of Tata Motors. This has resulted in more attention to Tata Group stocks from all over India. But again today, we are witnessing a fall in the prices of Tata Motors’ shares, which can be a negative signal for investors. So, if you want to learn how to analyze and predict the Tata Motors stock price. Predicting the stock market is one of the most important applications of Machine Learning in finance.

The ultimate goal is to predict the TATA motor stock prices using Machine Learning and the python programming language.

Step 1: Import all the required libraries

• Pandas : In computer programming, pandas is a software library written for the Python programming language for data manipulation and analysis and storing in a proper way. In particular, it offers data structures and operations for manipulating numerical tables and time series
• Sklearn : Scikit-learn (formerly scikits.learn) is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn.
• Pickle : Python pickle module is used for serializing and de-serializing a Python object structure. Pickling is a way to convert a python object (list, dict, etc.) into a character stream. The idea is that this character stream contains all the information necessary to reconstruct the object in another python script.
• Seaborn : Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
• Matplotlib : Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.

#Loading libraries 
import pandas as pd
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
import sklearn.linear_model 
import sklearn
import pickle
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import OneHotEncoder
from matplotlib.pyplot import figure
import matplotlib.pyplot as plt
from sklearn.metrics import mean_absolute_percentage_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.preprocessing import scale 
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, Lasso, LassoCV
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.decomposition import PCA
import warnings

warnings.filterwarnings('ignore')

Step 2 : Read dataset and basic details of dataset

Goal:- In this step we are going to read the dataset, view the dataset and analysis the basic details like total number of rows and columns, what are the column data types and see to need to create new column or not.

In this stage we are going to read our problem dataset and have a look on it.

#loading the dataset
try:
    df = pd.read_csv('F:\ML models\TATA Motors stock prediction\Data\TATAMOTORS.csv') #Path for the file
    print('Data read done successfully...')
except (FileNotFoundError, IOError):
    print("Wrong file or file path")
Data read done successfully...
# To view the content inside the dataset we can use the head() method that returns a specified number of rows, string from the top. 
# The head() method returns the first 5 rows if a number is not specified.
df.head()

Dataset: -

Attribute Information:

1. Close (closeing price of stock)

Five real-valued features are computed:

1. Date
2. Open (openning price of stock)
3. High (highest price of stock)
4. Low (lowest price of stock)
5. Adj Close
6. Volume

Step3: Data Preprocessing

Why need of Data Preprocessing?

Preprocessing data is an important step for data analysis. The following are some benefits of preprocessing data:

• It improves accuracy and reliability. Preprocessing data removes missing or inconsistent data values resulting from human or computer error, which can improve the accuracy and quality of a dataset, making it more reliable.
• It makes data consistent. When collecting data, it’s possible to have data duplicates, and discarding them during preprocessing can ensure the data values for analysis are consistent, which helps produce accurate results.
• It increases the data’s algorithm readability. Preprocessing enhances the data’s quality and makes it easier for machine learning algorithms to read, use, and interpret it.

After we read the data, we can look at the data using:

# count the total number of rows and columns.
print ('The train data has {0} rows and {1} columns'.format(df.shape[0],df.shape[1]))
The train data has 250 rows and 7 columns

By analysing the problem statement and the dataset, we get to know that the target variable is “close” column which says the value of stocks at closing.

df['Close'].value_counts()
452.049988    2
391.750000    2
423.500000    2
441.149994    2
435.649994    2
             ..
416.350006    1
430.850006    1
441.549988    1
437.049988    1
439.899994    1
Name: Close, Length: 232, dtype: int64

The df.value_counts() method counts the number of types of values a particular column contains.

df.shape
(250, 7)

The df.shape method shows the shape of the dataset.

We can identify that out of the 250 rows and 7 columns.

df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 250 entries, 0 to 249
Data columns (total 7 columns):
 #   Column     Non-Null Count  Dtype  
---  ------     --------------  -----  
 0   Date       250 non-null    object 
 1   Open       250 non-null    float64
 2   High       250 non-null    float64
 3   Low        250 non-null    float64
 4   Close      250 non-null    float64
 5   Adj Close  250 non-null    float64
 6   Volume     250 non-null    int64  
dtypes: float64(5), int64(1), object(1)
memory usage: 13.8+ KB

The df.info() method prints information about a DataFrame including the index dtype and columns, non-null values and memory usage.

We can identify that the close cloumn have continuous values. So, it is a regression problem.

df.iloc[1]
Date         2022-02-18
Open              498.0
High         502.649994
Low               491.5
Close        493.149994
Adj Close    493.149994
Volume         15476803
Name: 1, dtype: object

df.iloc[ ] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. The iloc property gets, or sets, the value(s) of the specified indexes.

Data Type Check for every column

Why data type check is required?

Data type check helps us with understanding what type of variables our dataset contains. It helps us with identifying whether to keep that variable or not. If the dataset contains contiguous data, then only float and integer type variables will be beneficial and if we have to classify any value then categorical variables will be beneficial.

objects_cols = ['object']
objects_lst = list(df.select_dtypes(include=objects_cols).columns)
print("Total number of categorical columns are ", len(objects_lst))
print("There names are as follows: ", objects_lst)
Total number of categorical columns are  1
There names are as follows:  ['Date']
int64_cols = ['int64']
int64_lst = list(df.select_dtypes(include=int64_cols).columns)
print("Total number of numerical columns are ", len(int64_lst))
print("There names are as follows: ", int64_lst)
Total number of numerical columns are  1
There names are as follows:  ['Volume']
float64_cols = ['float64']
float64_lst = list(df.select_dtypes(include=float64_cols).columns)
print("Total number of float64 columns are ", len(float64_lst))
print("There name are as follow: ", float64_lst)
Total number of float64 columns are  5
There name are as follow:  ['Open', 'High', 'Low', 'Close', 'Adj Close']

Step 2 Insights: -

1. We have total 7 features where 1 is object type, 1 is integer type while others are float type.

After this step we have to calculate various evaluation parameters which will help us in cleaning and analysing the data more accurately.

We identify that the 4 features open, high, low and close which represent the value of stock at different scenario.

Step 3: Descriptive Analysis

Goal/Purpose: Finding the data distribution of the features. Visualization helps to understand data and also to explain the data to another person.

Things we are going to do in this step:

1. Mean
2. Median
3. Mode
4. Standard Deviation
5. Variance
6. Null Values
7. NaN Values
8. Min value
9. Max value
10. Count Value
11. Quatilers
12. Correlation
13. Skewness

df.describe()

The df.describe() method returns description of the data in the DataFrame. If the DataFrame contains numerical or float data, the description contains these information for each column: count — The number of not-empty values. mean — The average (mean) value.

Measure the variability of data of the dataset

Variability describes how far apart data points lie from each other and from the center of a distribution.

1. Standard Deviation

The standard deviation is the average amount of variability in your dataset.

It tells you, on average, how far each data point lies from the mean. The larger the standard deviation, the more variable the data set is and if zero variance then there is no variability in the dataset that means there no use of that dataset.

So, it helps in understanding the measurements when the data is distributed. The more the data is distributed, the greater will be the standard deviation of that data.Here, you as an individual can determine which company is beneficial in long term. But, if you didn’t know the SD you would have choosen a wrong compnay for you.

df.std()
Open         2.432455e+01
High         2.454784e+01
Low          2.442464e+01
Close        2.456739e+01
Adj Close    2.456739e+01
Volume       1.062443e+07
dtype: float64

We can also understand the standard deviation using the below function.

def std_cal(df,float64_lst):
    
    cols = ['normal_value', 'zero_value']
    zero_value = 0
    normal_value = 0
    
    for value in float64_lst:
    
        rs = round(df[value].std(),6)

        if rs > 0:
            normal_value = normal_value + 1

        elif rs == 0:
            zero_value = zero_value + 1
    
    std_total_df =  pd.DataFrame([[normal_value, zero_value]], columns=cols) 
    
    return std_total_df
std_cal(df, float64_lst)

zero_value -> is the zero variance and when then there is no variability in the dataset that means there no use of that dataset.

2. Variance

The variance is the average of squared deviations from the mean. A deviation from the mean is how far a score lies from the mean.

Variance is the square of the standard deviation. This means that the units of variance are much larger than those of a typical value of a data set.

Why do we used Variance ?

By Squairng the number we get non-negative computation i.e. Disperson cannot be negative. The presence of variance is very important in your dataset because this will allow the model to learn about the different patterns hidden in the data

df.var()
Open         5.916838e+02
High         6.025964e+02
Low          5.965629e+02
Close        6.035568e+02
Adj Close    6.035568e+02
Volume       1.128785e+14
dtype: float64

We can also understand the Variance using the below function.

zero_cols = []

def var_cal(df,float64_lst):
    
    cols = ['normal_value', 'zero_value']
    zero_value = 0
    normal_value = 0
    
    for value in float64_lst:
    
        rs = round(df[value].var(),6)

        if rs > 0:
            normal_value = normal_value + 1

        elif rs == 0:
            zero_value = zero_value + 1
            zero_cols.append(value)
    
    var_total_df =  pd.DataFrame([[normal_value, zero_value]], columns=cols) 
    
    return var_total_df
var_cal(df, float64_lst)

zero_value -> Zero variance means that there is no difference in the data values, which means that they are all the same.

Measure central tendency

A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. As such, measures of central tendency are sometimes called measures of central location. They are also classed as summary statistics.

Mean — The average value. Median — The mid point value. Mode — The most common value.

1. Mean

The mean is the arithmetic average, and it is probably the measure of central tendency that you are most familiar.

Why do we calculate mean?

The mean is used to summarize a data set. It is a measure of the center of a data set.

df.mean()
Open         4.294704e+02
High         4.342746e+02
Low          4.232022e+02
Close        4.282946e+02
Adj Close    4.282946e+02
Volume       1.764996e+07
dtype: float64

We can also understand the mean using the below function.

def mean_cal(df,int64_lst):
    
    cols = ['normal_value', 'zero_value']
    zero_value = 0
    normal_value = 0
    
    for value in int64_lst:
    
        rs = round(df[value].mean(),6)

        if rs > 0:
            normal_value = normal_value + 1

        elif rs == 0:
            zero_value = zero_value + 1
    
    mean_total_df =  pd.DataFrame([[normal_value, zero_value]], columns=cols) 
    
    return mean_total_df
mean_cal(df, int64_lst)

mean_cal(df,float64_lst)

zero_value -> that the mean of a paticular column is zero, which isn’t usefull in anyway and need to be drop.

2.Median

The median is the middle value. It is the value that splits the dataset in half.The median of a dataset is the value that, assuming the dataset is ordered from smallest to largest, falls in the middle. If there are an even number of values in a dataset, the middle two values are the median.

Why do we calculate median ?

By comparing the median to the mean, you can get an idea of the distribution of a dataset. When the mean and the median are the same, the dataset is more or less evenly distributed from the lowest to highest values.

df.median()
Open         4.291250e+02
High         4.349500e+02
Low          4.235750e+02
Close        4.280000e+02
Adj Close    4.280000e+02
Volume       1.462807e+07
dtype: float64

We can also understand the median using the below function.

def median_cal(df,int64_lst):
    
    cols = ['normal_value', 'zero_value']
    zero_value = 0
    normal_value = 0
    
    for value in int64_lst:
    
        rs = round(df[value].mean(),6)

        if rs > 0:
            normal_value = normal_value + 1

        elif rs == 0:
            zero_value = zero_value + 1
    
    median_total_df =  pd.DataFrame([[normal_value, zero_value]], columns=cols) 
    
    return median_total_df
median_cal(df, int64_lst)

median_cal(df, float64_lst)

zero_value -> that the median of a paticular column is zero which isn’t usefull in anyway and need to be drop.

3. Mode

The mode is the value that occurs the most frequently in your data set. On a bar chart, the mode is the highest bar. If the data have multiple values that are tied for occurring the most frequently, you have a multimodal distribution. If no value repeats, the data do not have a mode.

Why do we calculate mode ?

The mode can be used to summarize categorical variables, while the mean and median can be calculated only for numeric variables. This is the main advantage of the mode as a measure of central tendency. It’s also useful for discrete variables and for continuous variables when they are expressed as intervals.

df.mode()

def mode_cal(df,int64_lst):
    
    cols = ['normal_value', 'zero_value', 'string_value']
    zero_value = 0
    normal_value = 0
    string_value = 0
    
    for value in float64_lst:
                
        rs = df[value].mode()[0]
        
        if isinstance(rs, str):
            string_value = string_value + 1
        else:

            if rs > 0:
                normal_value = normal_value + 1

            elif rs == 0:
                zero_value = zero_value + 1
    
    mode_total_df =  pd.DataFrame([[normal_value, zero_value, string_value]], columns=cols) 
    
    return mode_total_df
mode_cal(df, list(df.columns))

zero_value -> that the mode of a paticular column is zero which isn’t usefull in anyway and need to be drop.

Graphical representation of statistical analysis

sns.set_style('darkgrid')
color = 'royalblue'

plt.figure(figsize = (12,55))
i = 0
for index, col in enumerate(list(df.select_dtypes(include=['int64','float64']).columns)):
    i += 1 ;
    plt.subplot(21,2, index + i)
    ax = sns.histplot(x = col, data = df, color = "#326598", stat = "density", common_norm=False)
    sns.kdeplot(x = col, data = df, color = "pink", linewidth = 5)
    plt.xlabel(col, size = 15)
    plt.title('train')
    # set text on axes
    textstr_train = '\n'.join((
    r'$\mu=%.2f$' %df[col].mean(),
    r'$\sigma=%.2f$' %df[col].std(),
    r'$\mathrm{median}=%0.2f$' %np.median(df[col]),
    r'$\mathrm{min}=%.2f$' %df[col].min(),
    r'$\mathrm{max}=%.2f$' %df[col].max()
    ))
    ax.text(0.7, 0.90, textstr_train, transform=ax.transAxes, fontsize=10, verticalalignment='top',
                     bbox=dict(boxstyle='round',facecolor='pink', edgecolor='black', pad=0.5, alpha = 0.5))
    
    plt.subplot(21,2, index + (i+1))
    ax = sns.histplot(x = col, data = df, color = "#326598", stat = "density", common_norm=False)
    sns.kdeplot(x = col, data = df, color = "pink", linewidth = 5)
    plt.xlabel(col, size = 15)
    plt.title('test')
    
    textstr_test = '\n'.join((
    r'$\mu=%.2f$' %df[col].mean(),
    r'$\sigma=%.2f$' %df[col].std(),
    r'$\mathrm{median}=%0.2f$' %np.median(df[col]),
    r'$\mathrm{min}=%.2f$' %df[col].min(),
    r'$\mathrm{max}=%.2f$' %df[col].max()
    ))
    ax.text(0.7, 0.90, textstr_test, transform=ax.transAxes, fontsize=10, verticalalignment='top',bbox=dict(boxstyle='round',facecolor='pink', edgecolor='black', pad=0.5, alpha = 0.5))
    plt.grid()

plt.suptitle("Disturbution Of Features", y = 1, x = 0.55, size = 20,fontweight = "bold")
plt.tight_layout()
plt.show()

Null and Nan values

1. Null Values

A null value in a relational database is used when the value in a column is unknown or missing. A null is neither an empty string (for character or datetime data types) nor a zero value (for numeric data types).

df.isnull().sum()
Date         0
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64

As we notice that there are no null values in our dataset.

1. Nan Values

NaN, standing for Not a Number, is a member of a numeric data type that can be interpreted as a value that is undefined or unrepresentable, especially in floating-point arithmetic.

df.isna().sum()
Date         0
Open         0
High         0
Low          0
Close        0
Adj Close    0
Volume       0
dtype: int64

As we notice that there are no nan values in our dataset.

Another way to remove null and nan values is to use the method “df.dropna(inplace=True)”.

Count of unique occurences of every value in all categorical value

for value in objects_lst:
    
    print(f"{value:{10}} {df[value].value_counts()}")
Date       2022-02-17    1
2022-10-28    1
2022-10-10    1
2022-10-11    1
2022-10-12    1
             ..
2022-06-28    1
2022-06-29    1
2022-06-30    1
2022-07-01    1
2023-02-17    1
Name: Date, Length: 250, dtype: int64

• Categorical data are variables that contain label values rather than numeric values.The number of possible values is often limited to a fixed set.
• Use Label Encoder to label the categorical data. Label Encoder is the part of SciKit Learn library in Python and used to convert categorical data, or text data, into numbers, which our predictive models can better understand.

Label Encoding refers to converting the labels into a numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning.

Skewness

Skewness is a measure of the asymmetry of a distribution. A distribution is asymmetrical when its left and right side are not mirror images. A distribution can have right (or positive), left (or negative), or zero skewness

Why do we calculate Skewness ?

Skewness gives the direction of the outliers if it is right-skewed, most of the outliers are present on the right side of the distribution while if it is left-skewed, most of the outliers will present on the left side of the distribution

Below is the function to calculate skewness.

def right_nor_left(df, int64_lst):
    
    temp_skewness = ['column', 'skewness_value', 'skewness (+ve or -ve)']
    temp_skewness_values  = []
    
    temp_total = ["positive (+ve) skewed", "normal distrbution" , "negative (-ve) skewed"]
    positive = 0
    negative = 0
    normal = 0
    
    for value in int64_lst:
    
        rs = round(df[value].skew(),4)

        if rs > 0:
            temp_skewness_values.append([value,rs , "positive (+ve) skewed"])   
            positive = positive + 1

        elif rs == 0:
            temp_skewness_values.append([value,rs,"normal distrbution"])
            normal = normal + 1

        elif rs < 0:
            temp_skewness_values.append([value,rs, "negative (-ve) skewed"])
            negative = negative + 1
    
    skewness_df =  pd.DataFrame(temp_skewness_values, columns=temp_skewness) 
    skewness_total_df =  pd.DataFrame([[positive, normal, negative]], columns=temp_total) 
    
    return skewness_df, skewness_total_df
float64_cols = ['float64']
float64_lst_col = list(df.select_dtypes(include=float64_cols).columns)

skew_df,skew_total_df = right_nor_left(df, float64_lst_col)
skew_df

skew_total_df

float64_cols = ['int64','int32']
float64_lst_col = list(df.select_dtypes(include=float64_cols).columns)

skew_df,skew_total_df = right_nor_left(df, float64_lst_col)
skew_df

skew_total_df

We notice with the above results that we have following details:

1. 6 columns are positive skewed

Step 3 Insights: -

With the statistical analysis we have found that the data have a lot of skewness in them, some of the columns are positively skewed while some are negative skewed. We have used cube root transformation to transform the skewness.

Statistical analysis is little difficult to understand at one glance so to make it more understandable we will perform visulatization on the data which will help us to understand the process easily.

Why we are calculating all these metrics?

Mean / Median /Mode/ Variance /Standard Deviation are all very basic but very important concept of statistics used in data science. Almost all the machine learning algorithm uses these concepts in data preprocessing steps. These concepts are part of descriptive statistics where we basically used to describe and understand the data for features in Machine learning

Why is Stock Market Prediction Important?

Some of the top advantages associated with the stock market prediction:-

• Removes the Investment Bias.
• Develops the Habit of Complete Analysis.
• Minimizes Your Losses.
• Assures Consistency.
• Gives a Better Idea about Entry and Exit Points.
• Allows the Smart Way of Making Money.

Step 4: Data Exploration

Goal/Purpose:

Graphs we are going to develop in this step

1. Histogram of all columns to check the distrubution of the columns
2. Distplot or distribution plot of all columns to check the variation in the data distribution
3. Heatmap to calculate correlation within feature variables
4. Boxplot to find out outlier in the feature columns
5. Scatter Plot to show the relation between variables
6. Jointplot

1. Histogram

A histogram is a bar graph-like representation of data that buckets a range of classes into columns along the horizontal x-axis.The vertical y-axis represents the number count or percentage of occurrences in the data for each column

# Distribution in attributes
%matplotlib inline
for i in df.columns:
    df[i].hist(bins=50, figsize=(5,5))
    plt.title(i+"\n",fontweight ="bold")
    plt.show()
    print("   =======================================================================================================\n")

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Histogram Insight: -

Histogram helps in identifying the following:

• View the shape of your data set’s distribution to look for outliers or other significant data points.
• Determine whether something significant has boccurred from one time period to another.

Why Histogram?

It is used to illustrate the major features of the distribution of the data in a convenient form. It is also useful when dealing with large data sets (greater than 100 observations). It can help detect any unusual observations (outliers) or any gaps in the data.

From the above graphical representation we can identify that the highest bar represents the outliers which is above the maximum range.

We can also identify that the values are moving on the right side, which determines positive and the centered values determines normal skewness.

2. Distplot

A Distplot or distribution plot, depicts the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables. The Seaborn module along with the Matplotlib module is used to depict the distplot with different variations in it

num = [f for f in df.columns if df.dtypes[f] != 'object']
nd = pd.melt(df, value_vars = num)
n1 = sns.FacetGrid (nd, col='variable', col_wrap=3, sharex=False, sharey = False)
n1 = n1.map(sns.distplot, 'value')
n1
<seaborn.axisgrid.FacetGrid at 0x20115ecacb0>

df.skew()
Open         0.377781
High         0.392227
Low          0.305637
Close        0.328589
Adj Close    0.328589
Volume       2.379818
dtype: float64

Distplot Insights: -

Above is the distrution bar graphs to confirm about the statistics of the data about the skewness, the above results are:

1. 6 columns are positive skewed
2. 1 column is added here i.e Close which is our target variable which is also +ve skewed. In that case we’ll need to log transform this variable so that it becomes normally distributed. A normally distributed (or close to normal) target variable helps in better modeling the relationship between target and independent variables

Why Distplot?

Skewness is demonstrated on a bell curve when data points are not distributed symmetrically to the left and right sides of the median on a bell curve. If the bell curve is shifted to the left or the right, it is said to be skewed.

We can observe that the bell curve is shifted to left we indicates positive skewness.As all the column are positively skewed we don’t need to do scaling.

Let’s proceed and check the distribution of the target variable.

#+ve skewed 
df['Close'].skew()
0.3285887193112172

The target variable is positively skewed.A normally distributed (or close to normal) target variable helps in better modeling the relationship between target and independent variables.

3. Heatmap

A heatmap (or heat map) is a graphical representation of data where values are depicted by color.Heatmaps make it easy to visualize complex data and understand it at a glance

Correlation — A positive correlation is a relationship between two variables in which both variables move in the same direction. Therefore, when one variable increases as the other variable increases, or one variable decreases while the other decreases.

Correlation can have a value:

• 1 is a perfect positive correlation
• 0 is no correlation (the values don’t seem linked at all)
• -1 is a perfect negative correlation

#correlation plot
sns.set(rc = {'figure.figsize':(10,5)})
corr = df.corr().abs()
sns.heatmap(corr,annot=True) 
plt.show()

Notice the last column from right side of this map. We can see the correlation of all variables against Close. As you can see, some variables seem to be strongly correlated with the target variable. Here, a numeric correlation score will help us understand the graph better.

print (corr['Close'].sort_values(ascending=False)[:15], '\n') #top 15 values
print ('----------------------')
print (corr['Close'].sort_values(ascending=False)[-5:]) #last 5 values`
Close        1.000000
Adj Close    1.000000
Low          0.984145
High         0.983082
Open         0.958882
Volume       0.053984
Name: Close, dtype: float64 

----------------------
Adj Close    1.000000
Low          0.984145
High         0.983082
Open         0.958882
Volume       0.053984
Name: Close, dtype: float64

Here we see that the Adj Close features are 100% correlated with the target variable.

corr

Heatmap insights: -

As we know, it is recommended to avoid correlated features in your dataset. Indeed, a group of highly correlated features will not bring additional information (or just very few), but will increase the complexity of the algorithm, hence increasing the risk of errors.

Why Heatmap?

Heatmaps are used to show relationships between two variables, one plotted on each axis. By observing how cell colors change across each axis, you can observe if there are any patterns in value for one or both variables.

4. Boxplot

A boxplot is a standardized way of displaying the distribution of data based on a five number summary (“minimum”, first quartile [Q1], median, third quartile [Q3] and “maximum”).

Basically, to find the outlier in a dataset/column.

features = ['Open','High','Low','Adj Close','Volume']
for value in features:
    sns.catplot(data=df, x=value,  kind="box")

#for target variable
sns.catplot(data=df, x='Close', kind='box')
<seaborn.axisgrid.FacetGrid at 0x201127957e0>

The dark points are known as Outliers. Outliers are those data points that are significantly different from the rest of the dataset. They are often abnormal observations that skew the data distribution, and arise due to inconsistent data entry, or erroneous observations.

Boxplot Insights: -

• Sometimes outliers may be an error in the data and should be removed. In this case these points are correct readings yet they are different from the other points that they appear to be incorrect.
• The best way to decide wether to remove them or not is to train models with and without these data points and compare their validation accuracy.
• So we will keep it unchanged as it won’t affect our model.

Here, we can see that most of the variables possess outlier values. It would take us days if we start treating these outlier values one by one. Hence, for now we’ll leave them as is and let our algorithm deal with them. As we know, tree-based algorithms are usually robust to outliers.

Why Boxplot?

Box plots are used to show distributions of numeric data values, especially when you want to compare them between multiple groups. They are built to provide high-level information at a glance, offering general information about a group of data’s symmetry, skew, variance, and outliers.

6. Jointplot

for i in features:
    sns.jointplot(x=i, y='Close', data=df, kind='scatter', color='red')

In the next step we will divide our cleaned data into training data and testing data.

Step 2: Data Preparation

Goal:-

Tasks we are going to in this step:

1. Now we will spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.
2. Split dataset into train and test dataset.
3. Scaling on train dataset.

1. Now we spearate the target variable and feature columns in two different dataframe and will check the shape of the dataset for validation purpose.

# Separate target and feature column in X and y variable
target = 'Close'

# X will be the features
X = df.drop(['Close','Adj Close','Date'],axis=1) 
#y will be the target variable
y = df[target]

y have target variable and X have all other variable.

Here in TATA motors stock price prediction, close is the target variable.

X.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 250 entries, 0 to 249
Data columns (total 4 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   Open    250 non-null    float64
 1   High    250 non-null    float64
 2   Low     250 non-null    float64
 3   Volume  250 non-null    int64  
dtypes: float64(3), int64(1)
memory usage: 7.9 KB
y
0      499.950012
1      493.149994
2      494.450012
3      478.250000
4      477.000000
          ...    
245    441.049988
246    440.549988
247    444.149994
248    441.600006
249    439.899994
Name: Close, Length: 250, dtype: float64
# Check the shape of X and y variable
X.shape, y.shape
((250, 4), (250,))
# Reshape the y variable 
y = y.values.reshape(-1,1)
# Again check the shape of X and y variable
X.shape, y.shape
((250, 4), (250, 1))

2. Spliting the dataset in training and testing data.

Here we are spliting our dataset into 80/20 percentage where 80% dataset goes into the training part and 20% goes into testing part.

# Split the X and y into X_train, X_test, y_train, y_test variables with 80-20% split.
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Check shape of the splitted variables
X_train.shape, X_test.shape, y_train.shape, y_test.shape
((200, 4), (50, 4), (200, 1), (50, 1))

Insights: -

Train test split technique is used to estimate the performance of machine learning algorithms which are used to make predictions on data not used to train the model.It is a fast and easy procedure to perform, the results of which allow you to compare the performance of machine learning algorithms for your predictive modeling problem. Although simple to use and interpret, there are times when the procedure should not be used, such as when you have a small dataset and situations where additional configuration is required, such as when it is used for classification and the dataset is not balanced.

In the next step we will train our model on the basis of our training and testing data.

Step 3: Model Training

Goal:

In this step we are going to train our dataset on different regression algorithms. As we know that our target variable is not discrete format so we have to apply regression algorithm. In our dataset we have the outcome variable or Dependent variable i.e Y having non discrete values. So we will use Regression algorithm.

Algorithms we are going to use in this step

1. Linear Regression
2. Lasso Regression
3. Ridge Regression

K-fold cross validation is a procedure used to estimate the skill of the model on new data. There are common tactics that you can use to select the value of k for your dataset. There are commonly used variations on cross-validation, such as stratified and repeated, that are available in scikit-learn

# Define kfold with 10 split
cv = KFold(n_splits=10, shuffle=True, random_state=42)

The goal of cross-validation is to test the model’s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).

1. Linear Regression

Linear regression is one of the easiest and most popular Machine Learning algorithms. It is a statistical method that is used for predictive analysis. Linear regression makes predictions for continuous/real or numeric variables.

Train set cross-validation

#Using Linear Regression Algorithm to the Training Set
from sklearn.linear_model import LinearRegression

log_R = LinearRegression() #Object Creation

log_R.fit(X_train, y_train)
LinearRegression()
#Accuracy check of trainig data

#Get R2 score
log_R.score(X_train, y_train)
0.9878398614769165
#Accuracy of test data
log_R.score(X_test, y_test)
0.9865743320943736
# Getting kfold values
lg_scores = -1 * cross_val_score(log_R, 
                                 X_train, 
                                 y_train, 
                                 cv=cv, 
                                 scoring='neg_root_mean_squared_error')
lg_scores
array([2.84844927, 2.61384806, 2.66739369, 3.98012595, 2.80045316,
       3.19061516, 1.72760544, 2.69870321, 2.52003998, 3.02780253])
# Mean of the train kfold scores
lg_score_train = np.mean(lg_scores)
lg_score_train
2.807503644870697

Prediction

Now we will perform prediction on the dataset using Linear Regression.

# Predict the values on X_test_scaled dataset 
y_predicted = log_R.predict(X_test)

Various parameters are calculated for analysing the predictions.

1. Confusion Matrix 2)Classification Report 3)Accuracy Score 4)Precision Score 5)Recall Score 6)F1 Score

Confusion Matrix

A confusion matrix presents a table layout of the different outcomes of the prediction and results of a classification problem and helps visualize its outcomes. It plots a table of all the predicted and actual values of a classifier.

This diagram helps in understanding the concept of confusion matrix.

Evaluating all kinds of evaluating parameters.

Classification Report :

A classification report is a performance evaluation metric in machine learning. It is used to show the precision, recall, F1 Score, and support of your trained classification model.

F1_score :

The F1 score is a machine learning metric that can be used in classification models.

Precision_score :

The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative. The best value is 1 and the worst value is 0.

Recall_score :

Recall score is used to measure the model performance in terms of measuring the count of true positives in a correct manner out of all the actual positive values. Precision-Recall score is a useful measure of success of prediction when the classes are very imbalanced.

# Evaluating the classifier
# printing every score of the classifier
# scoring in anything
from sklearn.metrics import r2_score  

li = r2_score(y_test, y_predicted)*100
print("The model used is Linear Regression")
print("R2 Score is: -")
print()
print(li)
The model used is Linear Regression
R2 Score is: -

98.65743320943736

2. Lasso Regression

Lasso regression is also called Penalized regression method. This method is usually used in machine learning for the selection of the subset of variables. It provides greater prediction accuracy as compared to other regression models. Lasso Regularization helps to increase model interpretation.

#Using Lasso Regression
from sklearn import linear_model
la = linear_model.Lasso(alpha=0.1)
#looking for training data
la.fit(X_train,y_train)
Lasso(alpha=0.1)
#Accuracy check of trainig data
la.score(X_train, y_train)
0.987837260826808

Prediction

# Predict the values on X_test_scaled dataset 
y_predicted=la.predict(X_test)

Evaluating all kinds of evaluating parameters.

#Accuracy check of test data
lg = r2_score(y_test,y_predicted)*100
print("The model used is Lasso Regression")
print("R2 Score is: -")
print()
print(lg)
The model used is Lasso Regression
R2 Score is: -

98.64209289422429

3. Ridge Regression

Ridge regression is used when there are multiple variables that are highly correlated. It helps to prevent overfitting by penalizing the coefficients of the variables. Ridge regression reduces the overfitting by adding a penalty term to the error function that shrinks the size of the coefficients.

#Using Ridge Regression
from sklearn.linear_model import Ridge
ri = Ridge(alpha=1.0)
#looking for training data
ri.fit(X_train,y_train)
Ridge()
#Accuracy check of trainig data
ri.score(X_train, y_train)
0.987839856892322

Prediction

# Predict the values on X_test_scaled dataset 

y_predicted=ri.predict(X_test)

Evaluating all kinds of evaluating parameters.

#Accuracy check of test data
rid = r2_score(y_test,y_predicted)*100
print("The model used is Ridge Regression")
print("R2 Score is: -")
print()
print(rid)
The model used is Ridge Regression
R2 Score is: -

98.65682990777104

Insight: -

cal_metric=pd.DataFrame([li,lg,rid],columns=["Score in percentage"])
cal_metric.index=['Linear Regression',
                  'Lasso Regression',
                  'Ridge Regression']
cal_metric

• As you can see with our Linear Regression(0.9865 or 98.65%) we are getting a better result.
• So we gonna save our model with Lasso Regression Algorithm.

To predict the stock price for the next few days we can use AutoTs Machine Learning Algorithm.

from autots import AutoTS
model = AutoTS(forecast_length=5, frequency='infer', ensemble='simple')
model = model.fit(df, date_col='Date', value_col='Close', id_col=None)
prediction = model.predict()
forecast = prediction.forecast
print(forecast)
Inferred frequency is: B
Model Number: 1 with model ARIMA in generation 0 of 10
Model Number: 2 with model AverageValueNaive in generation 0 of 10
Model Number: 3 with model AverageValueNaive in generation 0 of 10
Model Number: 4 with model AverageValueNaive in generation 0 of 10
Model Number: 5 with model DatepartRegression in generation 0 of 10
Model Number: 6 with model DatepartRegression in generation 0 of 10
Model Number: 7 with model DatepartRegression in generation 0 of 10
Model Number: 8 with model DatepartRegression in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 8: DatepartRegression
Model Number: 9 with model ETS in generation 0 of 10
Model Number: 10 with model ETS in generation 0 of 10
Model Number: 11 with model GLM in generation 0 of 10
Model Number: 12 with model GLM in generation 0 of 10
Model Number: 13 with model GLS in generation 0 of 10
Model Number: 14 with model GLS in generation 0 of 10
Model Number: 15 with model GluonTS in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 15: GluonTS
Model Number: 16 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 16: GluonTS
Model Number: 17 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 17: GluonTS
Model Number: 18 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 18: GluonTS
Model Number: 19 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 19: GluonTS
Model Number: 20 with model LastValueNaive in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 20: LastValueNaive
Model Number: 21 with model LastValueNaive in generation 0 of 10
Model Number: 22 with model LastValueNaive in generation 0 of 10
Model Number: 23 with model LastValueNaive in generation 0 of 10
Model Number: 24 with model SeasonalNaive in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 24: SeasonalNaive
Model Number: 25 with model SeasonalNaive in generation 0 of 10
Model Number: 26 with model SeasonalNaive in generation 0 of 10
Model Number: 27 with model UnobservedComponents in generation 0 of 10
Model Number: 28 with model UnobservedComponents in generation 0 of 10
Model Number: 29 with model UnobservedComponents in generation 0 of 10
Model Number: 30 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 30: VAR
Model Number: 31 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 31: VAR
Model Number: 32 with model VECM in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 32: VECM
Model Number: 33 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 33: VECM
Model Number: 34 with model WindowRegression in generation 0 of 10
Model Number: 35 with model ConstantNaive in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 35: ConstantNaive
Model Number: 36 with model FBProphet in generation 0 of 10
Template Eval Error: ModuleNotFoundError("No module named 'fbprophet'") in model 36: FBProphet
Model Number: 37 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 37: GluonTS
Model Number: 38 with model MultivariateRegression in generation 0 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 39 with model MultivariateRegression in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but not future_regressor supplied.") in model 39: MultivariateRegression
Model Number: 40 with model DatepartRegression in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 40: DatepartRegression
Model Number: 41 with model SeasonalNaive in generation 0 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 42 with model DatepartRegression in generation 0 of 10
Model Number: 43 with model UnobservedComponents in generation 0 of 10
Model Number: 44 with model UnobservedComponents in generation 0 of 10
Model Number: 45 with model ETS in generation 0 of 10
Model Number: 46 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 46: VECM
Model Number: 47 with model ARDL in generation 0 of 10
Model Number: 48 with model MultivariateMotif in generation 0 of 10
Model Number: 49 with model MultivariateMotif in generation 0 of 10
Model Number: 50 with model UnivariateMotif in generation 0 of 10
Model Number: 51 with model UnivariateMotif in generation 0 of 10
Model Number: 52 with model SectionalMotif in generation 0 of 10
Model Number: 53 with model SectionalMotif in generation 0 of 10
Model Number: 54 with model MultivariateRegression in generation 0 of 10
Model Number: 55 with model FBProphet in generation 0 of 10
Template Eval Error: ModuleNotFoundError("No module named 'fbprophet'") in model 55: FBProphet
Model Number: 56 with model SeasonalNaive in generation 0 of 10
Model Number: 57 with model DatepartRegression in generation 0 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 58 with model NVAR in generation 0 of 10


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 59 with model Theta in generation 0 of 10
Model Number: 60 with model UnivariateRegression in generation 0 of 10
Template Eval Error: ValueError("Input contains NaN, infinity or a value too large for dtype('float32').") in model 60: UnivariateRegression
Model Number: 61 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 61: ARCH
Model Number: 62 with model ConstantNaive in generation 0 of 10
Model Number: 63 with model LastValueNaive in generation 0 of 10
Model Number: 64 with model AverageValueNaive in generation 0 of 10
Model Number: 65 with model GLS in generation 0 of 10
Model Number: 66 with model SeasonalNaive in generation 0 of 10
Model Number: 67 with model GLM in generation 0 of 10
Template Eval Error: ValueError('regression_type=user and no future_regressor passed') in model 67: GLM
Model Number: 68 with model ETS in generation 0 of 10
ETS error ValueError('endog must be strictly positive when usingmultiplicative trend or seasonal components.')
ETS failed on Close with ValueError('endog must be strictly positive when usingmultiplicative trend or seasonal components.')
Model Number: 69 with model FBProphet in generation 0 of 10
Template Eval Error: ModuleNotFoundError("No module named 'fbprophet'") in model 69: FBProphet
Model Number: 70 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 70: GluonTS
Model Number: 71 with model UnobservedComponents in generation 0 of 10
Model Number: 72 with model VAR in generation 0 of 10
HolidayTransformer: no anomalies detected.
Template Eval Error: ValueError('Only gave one variable to VAR') in model 72: VAR
Model Number: 73 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 73: VECM
Model Number: 74 with model ARIMA in generation 0 of 10
Model Number: 75 with model WindowRegression in generation 0 of 10
Template Eval Error: KeyError('model') in model 75: WindowRegression
Model Number: 76 with model DatepartRegression in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 76: DatepartRegression
Model Number: 77 with model UnivariateRegression in generation 0 of 10
Template Eval Error: ValueError("Input contains NaN, infinity or a value too large for dtype('float32').") in model 77: UnivariateRegression
Model Number: 78 with model MultivariateRegression in generation 0 of 10
Template Eval Error: ValueError('Some value(s) of y are out of the valid range for family PoissonDistribution') in model 78: MultivariateRegression
Model Number: 79 with model UnivariateMotif in generation 0 of 10
HolidayTransformer: no anomalies detected.
Model Number: 80 with model MultivariateMotif in generation 0 of 10
Model Number: 81 with model SectionalMotif in generation 0 of 10
Model Number: 82 with model NVAR in generation 0 of 10
Model Number: 83 with model Theta in generation 0 of 10
Model Number: 84 with model ARDL in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 84: ARDL
Model Number: 85 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 85: ARCH
Model Number: 86 with model MetricMotif in generation 0 of 10
Model Number: 87 with model ETS in generation 0 of 10
Model Number: 88 with model SectionalMotif in generation 0 of 10
Model Number: 89 with model LastValueNaive in generation 0 of 10
Model Number: 90 with model MetricMotif in generation 0 of 10
Model Number: 91 with model DatepartRegression in generation 0 of 10
Template Eval Error: ValueError('Model DatepartRegression returned NaN for one or more series. fail_on_forecast_nan=True') in model 91: DatepartRegression
Model Number: 92 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 92: VAR
Model Number: 93 with model SeasonalNaive in generation 0 of 10
Model Number: 94 with model ETS in generation 0 of 10
Model Number: 95 with model AverageValueNaive in generation 0 of 10
Model Number: 96 with model VAR in generation 0 of 10
Template Eval Error: IndexError('tuple index out of range') in model 96: VAR
Model Number: 97 with model GLS in generation 0 of 10
Model Number: 98 with model ARDL in generation 0 of 10
Model Number: 99 with model UnivariateMotif in generation 0 of 10
Model Number: 100 with model ARDL in generation 0 of 10
Model Number: 101 with model ETS in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 101: ETS
Model Number: 102 with model UnivariateMotif in generation 0 of 10
Model Number: 103 with model SeasonalNaive in generation 0 of 10
Model Number: 104 with model SeasonalNaive in generation 0 of 10
Model Number: 105 with model UnobservedComponents in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor supplied") in model 105: UnobservedComponents
Model Number: 106 with model ConstantNaive in generation 0 of 10
Model Number: 107 with model LastValueNaive in generation 0 of 10
Model Number: 108 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 108: VAR
Model Number: 109 with model UnobservedComponents in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor supplied") in model 109: UnobservedComponents
Model Number: 110 with model UnivariateMotif in generation 0 of 10
Model Number: 111 with model DatepartRegression in generation 0 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 112 with model MetricMotif in generation 0 of 10


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 113 with model AverageValueNaive in generation 0 of 10
Model Number: 114 with model ARDL in generation 0 of 10
Model Number: 115 with model ARDL in generation 0 of 10
Model Number: 116 with model SeasonalNaive in generation 0 of 10
Model Number: 117 with model SeasonalNaive in generation 0 of 10
Model Number: 118 with model ARDL in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 118: ARDL
Model Number: 119 with model ConstantNaive in generation 0 of 10
Model Number: 120 with model ARIMA in generation 0 of 10
Model Number: 121 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 121: ARCH
Model Number: 122 with model Theta in generation 0 of 10
Model Number: 123 with model UnivariateMotif in generation 0 of 10
Model Number: 124 with model UnivariateMotif in generation 0 of 10
Model Number: 125 with model DatepartRegression in generation 0 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 126 with model GLM in generation 0 of 10
Template Eval Error: ValueError('regression_type=user and no future_regressor passed') in model 126: GLM
Model Number: 127 with model LastValueNaive in generation 0 of 10


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 128 with model SectionalMotif in generation 0 of 10
Model Number: 129 with model ConstantNaive in generation 0 of 10
Model Number: 130 with model UnivariateMotif in generation 0 of 10
Model Number: 131 with model SeasonalNaive in generation 0 of 10
Model Number: 132 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 132: VAR
Model Number: 133 with model UnivariateMotif in generation 0 of 10
Model Number: 134 with model SeasonalNaive in generation 0 of 10
Model Number: 135 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 135: ARCH
Model Number: 136 with model LastValueNaive in generation 0 of 10
Model Number: 137 with model WindowRegression in generation 0 of 10
Model Number: 138 with model GLM in generation 0 of 10
Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 138: GLM
Model Number: 139 with model Theta in generation 0 of 10
Model Number: 140 with model ARDL in generation 0 of 10
Model Number: 141 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 141: ARCH
Model Number: 142 with model MetricMotif in generation 0 of 10
Model Number: 143 with model NVAR in generation 0 of 10
Model Number: 144 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 144: ARCH
Model Number: 145 with model MetricMotif in generation 0 of 10
Model Number: 146 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 146: ARCH
Model Number: 147 with model VAR in generation 0 of 10
Template Eval Error: IndexError('tuple index out of range') in model 147: VAR
Model Number: 148 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 148: GluonTS
Model Number: 149 with model DatepartRegression in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 149: DatepartRegression
Model Number: 150 with model VAR in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VAR') in model 150: VAR
Model Number: 151 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 151: VECM
Model Number: 152 with model AverageValueNaive in generation 0 of 10
Model Number: 153 with model Theta in generation 0 of 10
Model Number: 154 with model GLM in generation 0 of 10
Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 154: GLM
Model Number: 155 with model AverageValueNaive in generation 0 of 10
Model Number: 156 with model VAR in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 156: VAR
Model Number: 157 with model LastValueNaive in generation 0 of 10
Template Eval Error: ValueError("Model returned NaN due to a preprocessing transformer {'fillna': 'rolling_mean_24', 'transformations': {'0': 'ScipyFilter', '1': 'SeasonalDifference', '2': 'AlignLastValue'}, 'transformation_params': {'0': {'method': 'savgol_filter', 'method_args': {'window_length': 7, 'polyorder': 3, 'deriv': 1, 'mode': 'nearest'}}, '1': {'lag_1': 7, 'method': 'LastValue'}, '2': {'rows': 1, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}}}. fail_on_forecast_nan=True") in model 157: LastValueNaive
Model Number: 158 with model GluonTS in generation 0 of 10
Template Eval Error: ImportError('GluonTS installation not found or installed version is incompatible with AutoTS.') in model 158: GluonTS
Model Number: 159 with model UnivariateMotif in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 159: UnivariateMotif
Model Number: 160 with model MetricMotif in generation 0 of 10
Model Number: 161 with model ConstantNaive in generation 0 of 10
Model Number: 162 with model SectionalMotif in generation 0 of 10
Model Number: 163 with model Theta in generation 0 of 10
Model Number: 164 with model AverageValueNaive in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 164: AverageValueNaive
Model Number: 165 with model Theta in generation 0 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 165: Theta
Model Number: 166 with model ETS in generation 0 of 10
Model Number: 167 with model GLM in generation 0 of 10
Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 167: GLM
Model Number: 168 with model MultivariateMotif in generation 0 of 10
Model Number: 169 with model ConstantNaive in generation 0 of 10
Template Eval Error: Exception('Transformer DifferencedTransformer failed on inverse') in model 169: ConstantNaive
Model Number: 170 with model ARCH in generation 0 of 10
Template Eval Error: ImportError('`arch` package must be installed from pip') in model 170: ARCH
Model Number: 171 with model FBProphet in generation 0 of 10
Template Eval Error: ModuleNotFoundError("No module named 'fbprophet'") in model 171: FBProphet
Model Number: 172 with model AverageValueNaive in generation 0 of 10
Model Number: 173 with model MultivariateMotif in generation 0 of 10
Model Number: 174 with model Theta in generation 0 of 10
Model Number: 175 with model GLM in generation 0 of 10
Model Number: 176 with model UnivariateMotif in generation 0 of 10
Model Number: 177 with model ARDL in generation 0 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 177: ARDL
Model Number: 178 with model MultivariateMotif in generation 0 of 10
Model Number: 179 with model NVAR in generation 0 of 10
Model Number: 180 with model UnivariateMotif in generation 0 of 10
Model Number: 181 with model Theta in generation 0 of 10
Model Number: 182 with model SectionalMotif in generation 0 of 10
Model Number: 183 with model ARIMA in generation 0 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 183: ARIMA
Model Number: 184 with model VECM in generation 0 of 10
Template Eval Error: ValueError('Only gave one variable to VECM') in model 184: VECM
Model Number: 185 with model NVAR in generation 0 of 10
Model Number: 186 with model SeasonalNaive in generation 0 of 10
New Generation: 1 of 10
Model Number: 187 with model DatepartRegression in generation 1 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 188 with model UnobservedComponents in generation 1 of 10
Model Number: 189 with model ETS in generation 1 of 10
Model Number: 190 with model ETS in generation 1 of 10
Model Number: 191 with model DatepartRegression in generation 1 of 10
Model Number: 192 with model NVAR in generation 1 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 192: NVAR
Model Number: 193 with model Theta in generation 1 of 10
Model Number: 194 with model AverageValueNaive in generation 1 of 10
Model Number: 195 with model MetricMotif in generation 1 of 10
Model Number: 196 with model Theta in generation 1 of 10
Model Number: 197 with model ConstantNaive in generation 1 of 10
Model Number: 198 with model NVAR in generation 1 of 10
Model Number: 199 with model MultivariateMotif in generation 1 of 10
Model Number: 200 with model UnobservedComponents in generation 1 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 200: UnobservedComponents
Model Number: 201 with model SeasonalNaive in generation 1 of 10
Model Number: 202 with model ARIMA in generation 1 of 10
Model Number: 203 with model LastValueNaive in generation 1 of 10
Model Number: 204 with model NVAR in generation 1 of 10
Model Number: 205 with model SeasonalNaive in generation 1 of 10
Model Number: 206 with model UnivariateMotif in generation 1 of 10
Model Number: 207 with model NVAR in generation 1 of 10
Model Number: 208 with model ARDL in generation 1 of 10
Model Number: 209 with model Theta in generation 1 of 10
Model Number: 210 with model GLM in generation 1 of 10
Template Eval Error: PerfectSeparationError('Perfect separation detected, results not available') in model 210: GLM
Model Number: 211 with model WindowRegression in generation 1 of 10
Template Eval Error: ModuleNotFoundError("No module named 'lightgbm'") in model 211: WindowRegression
Model Number: 212 with model AverageValueNaive in generation 1 of 10
Model Number: 213 with model DatepartRegression in generation 1 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 214 with model MetricMotif in generation 1 of 10


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 215 with model DatepartRegression in generation 1 of 10
Template Eval Error: ValueError('Model DatepartRegression returned NaN for one or more series. fail_on_forecast_nan=True') in model 215: DatepartRegression
Model Number: 216 with model ARIMA in generation 1 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 216: ARIMA
Model Number: 217 with model MetricMotif in generation 1 of 10
Model Number: 218 with model ConstantNaive in generation 1 of 10
Model Number: 219 with model LastValueNaive in generation 1 of 10
Model Number: 220 with model MetricMotif in generation 1 of 10
Model Number: 221 with model Theta in generation 1 of 10
Model Number: 222 with model ARIMA in generation 1 of 10
Model Number: 223 with model ARDL in generation 1 of 10
Model Number: 224 with model Theta in generation 1 of 10
Model Number: 225 with model ARDL in generation 1 of 10
Model Number: 226 with model UnivariateMotif in generation 1 of 10
Model Number: 227 with model UnivariateMotif in generation 1 of 10
Model Number: 228 with model UnivariateMotif in generation 1 of 10
Model Number: 229 with model GLS in generation 1 of 10
Model Number: 230 with model Theta in generation 1 of 10
Model Number: 231 with model SeasonalNaive in generation 1 of 10
Model Number: 232 with model AverageValueNaive in generation 1 of 10
Model Number: 233 with model Theta in generation 1 of 10
Model Number: 234 with model UnivariateMotif in generation 1 of 10
Model Number: 235 with model LastValueNaive in generation 1 of 10
Model Number: 236 with model UnobservedComponents in generation 1 of 10
Model Number: 237 with model NVAR in generation 1 of 10
Model Number: 238 with model ETS in generation 1 of 10
Model Number: 239 with model AverageValueNaive in generation 1 of 10
Model Number: 240 with model SectionalMotif in generation 1 of 10
Model Number: 241 with model UnobservedComponents in generation 1 of 10
Model Number: 242 with model GLS in generation 1 of 10
Model Number: 243 with model LastValueNaive in generation 1 of 10
Model Number: 244 with model SeasonalNaive in generation 1 of 10
Model Number: 245 with model SeasonalNaive in generation 1 of 10
Model Number: 246 with model UnivariateMotif in generation 1 of 10
Model Number: 247 with model MultivariateRegression in generation 1 of 10
Model Number: 248 with model MultivariateMotif in generation 1 of 10
Model Number: 249 with model MetricMotif in generation 1 of 10
Model Number: 250 with model Theta in generation 1 of 10
Model Number: 251 with model MetricMotif in generation 1 of 10
Model Number: 252 with model ETS in generation 1 of 10
ETS error ValueError('Can only dampen the trend component')
ETS failed on Close with ValueError('Can only dampen the trend component')
Model Number: 253 with model UnivariateMotif in generation 1 of 10
Model Number: 254 with model ARDL in generation 1 of 10
Model Number: 255 with model NVAR in generation 1 of 10
Model Number: 256 with model SectionalMotif in generation 1 of 10
Model Number: 257 with model Theta in generation 1 of 10
Model Number: 258 with model ARDL in generation 1 of 10
Model Number: 259 with model WindowRegression in generation 1 of 10
Model Number: 260 with model UnivariateMotif in generation 1 of 10
Model Number: 261 with model SeasonalNaive in generation 1 of 10
Model Number: 262 with model MultivariateMotif in generation 1 of 10
Model Number: 263 with model LastValueNaive in generation 1 of 10
Model Number: 264 with model UnivariateMotif in generation 1 of 10
Model Number: 265 with model ConstantNaive in generation 1 of 10
Template Eval Error: ValueError("Model returned NaN due to a preprocessing transformer {'fillna': 'rolling_mean_24', 'transformations': {'0': 'bkfilter', '1': 'MaxAbsScaler', '2': 'Detrend', '3': 'AlignLastValue', '4': 'bkfilter'}, 'transformation_params': {'0': {}, '1': {}, '2': {'model': 'Gamma', 'phi': 1, 'window': None, 'transform_dict': {'fillna': None, 'transformations': {'0': 'EWMAFilter'}, 'transformation_params': {'0': {'span': 2}}}}, '3': {'rows': 1, 'lag': 1, 'method': 'multiplicative', 'strength': 0.7, 'first_value_only': False}, '4': {}}}. fail_on_forecast_nan=True") in model 265: ConstantNaive
Model Number: 266 with model AverageValueNaive in generation 1 of 10
Model Number: 267 with model NVAR in generation 1 of 10
Model Number: 268 with model AverageValueNaive in generation 1 of 10
Model Number: 269 with model WindowRegression in generation 1 of 10
Model Number: 270 with model UnobservedComponents in generation 1 of 10
Model Number: 271 with model UnivariateMotif in generation 1 of 10
Model Number: 272 with model MetricMotif in generation 1 of 10
Model Number: 273 with model MultivariateMotif in generation 1 of 10
Model Number: 274 with model MetricMotif in generation 1 of 10
Model Number: 275 with model SectionalMotif in generation 1 of 10
Model Number: 276 with model Theta in generation 1 of 10
Model Number: 277 with model SeasonalNaive in generation 1 of 10
Model Number: 278 with model ConstantNaive in generation 1 of 10
Model Number: 279 with model UnobservedComponents in generation 1 of 10
Model Number: 280 with model SeasonalNaive in generation 1 of 10
Model Number: 281 with model LastValueNaive in generation 1 of 10
Model Number: 282 with model ARIMA in generation 1 of 10
Model Number: 283 with model AverageValueNaive in generation 1 of 10
Model Number: 284 with model MultivariateMotif in generation 1 of 10
Model Number: 285 with model ARDL in generation 1 of 10
Model Number: 286 with model SeasonalNaive in generation 1 of 10
Model Number: 287 with model MultivariateMotif in generation 1 of 10
Model Number: 288 with model NVAR in generation 1 of 10
Model Number: 289 with model UnobservedComponents in generation 1 of 10
Model Number: 290 with model AverageValueNaive in generation 1 of 10
Model Number: 291 with model MultivariateRegression in generation 1 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 292 with model ConstantNaive in generation 1 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 293 with model MultivariateRegression in generation 1 of 10
Model Number: 294 with model ETS in generation 1 of 10
ETS error ValueError('Can only dampen the trend component')
ETS failed on Close with ValueError('Can only dampen the trend component')
Model Number: 295 with model ETS in generation 1 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 295: ETS
Model Number: 296 with model ARDL in generation 1 of 10
Model Number: 297 with model GLS in generation 1 of 10
Model Number: 298 with model MetricMotif in generation 1 of 10
Model Number: 299 with model ARIMA in generation 1 of 10
Model Number: 300 with model DatepartRegression in generation 1 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 300: DatepartRegression
Model Number: 301 with model Theta in generation 1 of 10
Model Number: 302 with model GLS in generation 1 of 10
Model Number: 303 with model Theta in generation 1 of 10
Model Number: 304 with model UnobservedComponents in generation 1 of 10
Model Number: 305 with model SeasonalNaive in generation 1 of 10
Model Number: 306 with model AverageValueNaive in generation 1 of 10
Model Number: 307 with model UnivariateMotif in generation 1 of 10
Model Number: 308 with model ETS in generation 1 of 10
Model Number: 309 with model MultivariateRegression in generation 1 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 310 with model SectionalMotif in generation 1 of 10
Model Number: 311 with model NVAR in generation 1 of 10
New Generation: 2 of 10
Model Number: 312 with model MetricMotif in generation 2 of 10
Model Number: 313 with model UnivariateMotif in generation 2 of 10
Model Number: 314 with model AverageValueNaive in generation 2 of 10
Model Number: 315 with model LastValueNaive in generation 2 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 315: LastValueNaive
Model Number: 316 with model GLS in generation 2 of 10
Model Number: 317 with model ARDL in generation 2 of 10
Model Number: 318 with model NVAR in generation 2 of 10
Model Number: 319 with model NVAR in generation 2 of 10
Model Number: 320 with model Theta in generation 2 of 10
HolidayTransformer: no anomalies detected.
Model Number: 321 with model MetricMotif in generation 2 of 10
Model Number: 322 with model UnivariateMotif in generation 2 of 10
Model Number: 323 with model SectionalMotif in generation 2 of 10
Model Number: 324 with model SeasonalNaive in generation 2 of 10
Model Number: 325 with model Theta in generation 2 of 10
Model Number: 326 with model MultivariateRegression in generation 2 of 10
Template Eval Error: ModuleNotFoundError("No module named 'lightgbm'") in model 326: MultivariateRegression
Model Number: 327 with model NVAR in generation 2 of 10
Model Number: 328 with model UnivariateMotif in generation 2 of 10
Model Number: 329 with model DatepartRegression in generation 2 of 10
Model Number: 330 with model WindowRegression in generation 2 of 10
Model Number: 331 with model UnivariateMotif in generation 2 of 10
Model Number: 332 with model DatepartRegression in generation 2 of 10
Template Eval Error: ValueError('Model DatepartRegression returned NaN for one or more series. fail_on_forecast_nan=True') in model 332: DatepartRegression
Model Number: 333 with model ConstantNaive in generation 2 of 10
Model Number: 334 with model NVAR in generation 2 of 10
Model Number: 335 with model ETS in generation 2 of 10
Model Number: 336 with model ETS in generation 2 of 10
Model Number: 337 with model MetricMotif in generation 2 of 10
Model Number: 338 with model LastValueNaive in generation 2 of 10
Model Number: 339 with model LastValueNaive in generation 2 of 10
Model Number: 340 with model MultivariateMotif in generation 2 of 10
Model Number: 341 with model MultivariateMotif in generation 2 of 10
Model Number: 342 with model ARDL in generation 2 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 342: ARDL
Model Number: 343 with model MultivariateMotif in generation 2 of 10
Model Number: 344 with model ARDL in generation 2 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 344: ARDL
Model Number: 345 with model AverageValueNaive in generation 2 of 10
Model Number: 346 with model WindowRegression in generation 2 of 10
Template Eval Error: ValueError("Input contains NaN, infinity or a value too large for dtype('float64').") in model 346: WindowRegression
Model Number: 347 with model SectionalMotif in generation 2 of 10
Model Number: 348 with model UnobservedComponents in generation 2 of 10
Model Number: 349 with model MetricMotif in generation 2 of 10
Model Number: 350 with model LastValueNaive in generation 2 of 10
Model Number: 351 with model Theta in generation 2 of 10
Model Number: 352 with model ARDL in generation 2 of 10
Model Number: 353 with model SeasonalNaive in generation 2 of 10
Model Number: 354 with model AverageValueNaive in generation 2 of 10
Model Number: 355 with model ARIMA in generation 2 of 10
Model Number: 356 with model NVAR in generation 2 of 10
Model Number: 357 with model AverageValueNaive in generation 2 of 10
Model Number: 358 with model NVAR in generation 2 of 10
Model Number: 359 with model SeasonalNaive in generation 2 of 10
Model Number: 360 with model ARDL in generation 2 of 10
Model Number: 361 with model GLS in generation 2 of 10
Model Number: 362 with model Theta in generation 2 of 10
Model Number: 363 with model ARDL in generation 2 of 10
Model Number: 364 with model UnivariateMotif in generation 2 of 10
Model Number: 365 with model ARDL in generation 2 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 365: ARDL
Model Number: 366 with model LastValueNaive in generation 2 of 10
Model Number: 367 with model MultivariateMotif in generation 2 of 10
Model Number: 368 with model ARDL in generation 2 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 368: ARDL
Model Number: 369 with model MetricMotif in generation 2 of 10
Model Number: 370 with model MultivariateRegression in generation 2 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 371 with model ARDL in generation 2 of 10
Model Number: 372 with model MetricMotif in generation 2 of 10
Template Eval Error: TypeError("'NoneType' object is not subscriptable") in model 372: MetricMotif
Model Number: 373 with model AverageValueNaive in generation 2 of 10
Model Number: 374 with model ARIMA in generation 2 of 10
Model Number: 375 with model MultivariateRegression in generation 2 of 10
Model Number: 376 with model SeasonalNaive in generation 2 of 10
Model Number: 377 with model UnivariateMotif in generation 2 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 377: UnivariateMotif
Model Number: 378 with model MultivariateRegression in generation 2 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 378: MultivariateRegression
Model Number: 379 with model ARDL in generation 2 of 10
Model Number: 380 with model Theta in generation 2 of 10
Model Number: 381 with model NVAR in generation 2 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 381: NVAR
Model Number: 382 with model ETS in generation 2 of 10
Model Number: 383 with model GLS in generation 2 of 10
Model Number: 384 with model DatepartRegression in generation 2 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 384: DatepartRegression
Model Number: 385 with model AverageValueNaive in generation 2 of 10
Model Number: 386 with model MultivariateMotif in generation 2 of 10
Model Number: 387 with model MetricMotif in generation 2 of 10
Model Number: 388 with model MultivariateRegression in generation 2 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.6s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.6s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 389 with model ETS in generation 2 of 10
Model Number: 390 with model MetricMotif in generation 2 of 10
Model Number: 391 with model LastValueNaive in generation 2 of 10
Model Number: 392 with model UnobservedComponents in generation 2 of 10
Model Number: 393 with model UnivariateMotif in generation 2 of 10
Model Number: 394 with model WindowRegression in generation 2 of 10
Model Number: 395 with model GLS in generation 2 of 10
Model Number: 396 with model LastValueNaive in generation 2 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 396: LastValueNaive
Model Number: 397 with model SeasonalNaive in generation 2 of 10
Model Number: 398 with model Theta in generation 2 of 10
Model Number: 399 with model DatepartRegression in generation 2 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 400 with model MultivariateRegression in generation 2 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.6s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.7s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 401 with model MetricMotif in generation 2 of 10
Model Number: 402 with model UnobservedComponents in generation 2 of 10
Model Number: 403 with model AverageValueNaive in generation 2 of 10
Model Number: 404 with model SectionalMotif in generation 2 of 10
Model Number: 405 with model UnivariateMotif in generation 2 of 10
Model Number: 406 with model MetricMotif in generation 2 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 406: MetricMotif
Model Number: 407 with model ETS in generation 2 of 10
Model Number: 408 with model AverageValueNaive in generation 2 of 10
Model Number: 409 with model GLS in generation 2 of 10
Model Number: 410 with model UnivariateMotif in generation 2 of 10
Model Number: 411 with model ARIMA in generation 2 of 10
Model Number: 412 with model SeasonalNaive in generation 2 of 10
Model Number: 413 with model UnobservedComponents in generation 2 of 10
Model Number: 414 with model AverageValueNaive in generation 2 of 10
Model Number: 415 with model NVAR in generation 2 of 10
Model Number: 416 with model Theta in generation 2 of 10
Model Number: 417 with model NVAR in generation 2 of 10
Model Number: 418 with model MetricMotif in generation 2 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 418: MetricMotif
Model Number: 419 with model MetricMotif in generation 2 of 10
Model Number: 420 with model MultivariateMotif in generation 2 of 10
Model Number: 421 with model MultivariateRegression in generation 2 of 10
Model Number: 422 with model ETS in generation 2 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 422: ETS
Model Number: 423 with model SeasonalNaive in generation 2 of 10
Model Number: 424 with model ETS in generation 2 of 10
Model Number: 425 with model ETS in generation 2 of 10
Model Number: 426 with model MultivariateMotif in generation 2 of 10
Model Number: 427 with model NVAR in generation 2 of 10
Model Number: 428 with model GLS in generation 2 of 10
Model Number: 429 with model UnivariateMotif in generation 2 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 429: UnivariateMotif
Model Number: 430 with model UnivariateMotif in generation 2 of 10
Model Number: 431 with model SeasonalNaive in generation 2 of 10
Model Number: 432 with model Theta in generation 2 of 10
Model Number: 433 with model UnivariateMotif in generation 2 of 10
Model Number: 434 with model NVAR in generation 2 of 10
Model Number: 435 with model Theta in generation 2 of 10
Model Number: 436 with model MultivariateMotif in generation 2 of 10
New Generation: 3 of 10
Model Number: 437 with model DatepartRegression in generation 3 of 10
Template Eval Error: ValueError('Model DatepartRegression returned NaN for one or more series. fail_on_forecast_nan=True') in model 437: DatepartRegression
Model Number: 438 with model MultivariateMotif in generation 3 of 10
Model Number: 439 with model MetricMotif in generation 3 of 10
Model Number: 440 with model ARDL in generation 3 of 10
Model Number: 441 with model UnobservedComponents in generation 3 of 10
Model Number: 442 with model MetricMotif in generation 3 of 10
Model Number: 443 with model AverageValueNaive in generation 3 of 10
Model Number: 444 with model ARIMA in generation 3 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 444: ARIMA
Model Number: 445 with model SeasonalNaive in generation 3 of 10
Model Number: 446 with model SectionalMotif in generation 3 of 10
Model Number: 447 with model UnivariateMotif in generation 3 of 10
Model Number: 448 with model ARDL in generation 3 of 10
Model Number: 449 with model SeasonalNaive in generation 3 of 10
Model Number: 450 with model NVAR in generation 3 of 10
Model Number: 451 with model UnivariateMotif in generation 3 of 10
Model Number: 452 with model LastValueNaive in generation 3 of 10
Model Number: 453 with model Theta in generation 3 of 10
Model Number: 454 with model SeasonalNaive in generation 3 of 10
Model Number: 455 with model UnivariateMotif in generation 3 of 10
Model Number: 456 with model ARIMA in generation 3 of 10
Model Number: 457 with model LastValueNaive in generation 3 of 10
Model Number: 458 with model DatepartRegression in generation 3 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 458: DatepartRegression
Model Number: 459 with model GLM in generation 3 of 10
Template Eval Error: ValueError('regression_type=user and no future_regressor passed') in model 459: GLM
Model Number: 460 with model Theta in generation 3 of 10
Model Number: 461 with model ARIMA in generation 3 of 10
Model Number: 462 with model GLS in generation 3 of 10
Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 462: GLS
Model Number: 463 with model UnivariateMotif in generation 3 of 10
Model Number: 464 with model MultivariateRegression in generation 3 of 10
Model Number: 465 with model ARDL in generation 3 of 10
Model Number: 466 with model NVAR in generation 3 of 10
Model Number: 467 with model NVAR in generation 3 of 10
Model Number: 468 with model MetricMotif in generation 3 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 468: MetricMotif
Model Number: 469 with model ARDL in generation 3 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 469: ARDL
Model Number: 470 with model MetricMotif in generation 3 of 10
Model Number: 471 with model AverageValueNaive in generation 3 of 10
Model Number: 472 with model UnivariateMotif in generation 3 of 10
Template Eval Error: ValueError('Model UnivariateMotif returned NaN for one or more series. fail_on_forecast_nan=True') in model 472: UnivariateMotif
Model Number: 473 with model UnivariateMotif in generation 3 of 10
Model Number: 474 with model SeasonalNaive in generation 3 of 10
Model Number: 475 with model WindowRegression in generation 3 of 10
Template Eval Error: ModuleNotFoundError("No module named 'lightgbm'") in model 475: WindowRegression
Model Number: 476 with model UnivariateMotif in generation 3 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 476: UnivariateMotif
Model Number: 477 with model UnivariateMotif in generation 3 of 10
Model Number: 478 with model AverageValueNaive in generation 3 of 10
Model Number: 479 with model ARDL in generation 3 of 10
Model Number: 480 with model AverageValueNaive in generation 3 of 10
Model Number: 481 with model SectionalMotif in generation 3 of 10
Model Number: 482 with model ARIMA in generation 3 of 10
Model Number: 483 with model ETS in generation 3 of 10
Model Number: 484 with model MultivariateMotif in generation 3 of 10
Model Number: 485 with model SeasonalNaive in generation 3 of 10
Model Number: 486 with model MultivariateMotif in generation 3 of 10
Model Number: 487 with model NVAR in generation 3 of 10
Model Number: 488 with model Theta in generation 3 of 10
Model Number: 489 with model UnobservedComponents in generation 3 of 10
Template Eval Error: Exception('Transformer HolidayTransformer failed on fit') in model 489: UnobservedComponents
Model Number: 490 with model ARIMA in generation 3 of 10
Model Number: 491 with model DatepartRegression in generation 3 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 491: DatepartRegression
Model Number: 492 with model DatepartRegression in generation 3 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 493 with model Theta in generation 3 of 10
Model Number: 494 with model MetricMotif in generation 3 of 10
Model Number: 495 with model WindowRegression in generation 3 of 10
Model Number: 496 with model ARIMA in generation 3 of 10
Model Number: 497 with model MultivariateMotif in generation 3 of 10
Model Number: 498 with model DatepartRegression in generation 3 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 499 with model AverageValueNaive in generation 3 of 10


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 500 with model UnivariateMotif in generation 3 of 10
Model Number: 501 with model MultivariateMotif in generation 3 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 501: MultivariateMotif
Model Number: 502 with model ARDL in generation 3 of 10
Model Number: 503 with model NVAR in generation 3 of 10
Model Number: 504 with model MultivariateMotif in generation 3 of 10
Model Number: 505 with model ETS in generation 3 of 10
Model Number: 506 with model NVAR in generation 3 of 10
Model Number: 507 with model Theta in generation 3 of 10
Model Number: 508 with model Theta in generation 3 of 10
Model Number: 509 with model LastValueNaive in generation 3 of 10
Model Number: 510 with model SectionalMotif in generation 3 of 10
Model Number: 511 with model UnivariateMotif in generation 3 of 10
Model Number: 512 with model SeasonalNaive in generation 3 of 10
Model Number: 513 with model LastValueNaive in generation 3 of 10
Model Number: 514 with model MultivariateRegression in generation 3 of 10
Model Number: 515 with model Theta in generation 3 of 10
Model Number: 516 with model LastValueNaive in generation 3 of 10
Model Number: 517 with model AverageValueNaive in generation 3 of 10
Model Number: 518 with model UnivariateMotif in generation 3 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 518: UnivariateMotif
Model Number: 519 with model SeasonalNaive in generation 3 of 10
Model Number: 520 with model ETS in generation 3 of 10
Model Number: 521 with model UnobservedComponents in generation 3 of 10
Model Number: 522 with model MetricMotif in generation 3 of 10
Model Number: 523 with model MetricMotif in generation 3 of 10
Model Number: 524 with model ARDL in generation 3 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 524: ARDL
Model Number: 525 with model SeasonalNaive in generation 3 of 10
Model Number: 526 with model MetricMotif in generation 3 of 10
Model Number: 527 with model AverageValueNaive in generation 3 of 10
Model Number: 528 with model AverageValueNaive in generation 3 of 10
Model Number: 529 with model GLS in generation 3 of 10
Model Number: 530 with model GLS in generation 3 of 10
Model Number: 531 with model SectionalMotif in generation 3 of 10
Model Number: 532 with model NVAR in generation 3 of 10
Model Number: 533 with model MultivariateRegression in generation 3 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 534 with model UnobservedComponents in generation 3 of 10
Model Number: 535 with model MetricMotif in generation 3 of 10
Model Number: 536 with model ARIMA in generation 3 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 536: ARIMA
Model Number: 537 with model SectionalMotif in generation 3 of 10
Model Number: 538 with model MetricMotif in generation 3 of 10
Model Number: 539 with model AverageValueNaive in generation 3 of 10
Model Number: 540 with model NVAR in generation 3 of 10
Model Number: 541 with model NVAR in generation 3 of 10
Model Number: 542 with model ConstantNaive in generation 3 of 10
Model Number: 543 with model MultivariateMotif in generation 3 of 10
Model Number: 544 with model MultivariateRegression in generation 3 of 10
Model Number: 545 with model MultivariateMotif in generation 3 of 10
Model Number: 546 with model ARDL in generation 3 of 10
Model Number: 547 with model ETS in generation 3 of 10
Model Number: 548 with model MultivariateRegression in generation 3 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.3s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 549 with model ARDL in generation 3 of 10
Model Number: 550 with model MultivariateRegression in generation 3 of 10
Model Number: 551 with model AverageValueNaive in generation 3 of 10
Model Number: 552 with model MetricMotif in generation 3 of 10
Model Number: 553 with model ARIMA in generation 3 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 553: ARIMA
Model Number: 554 with model AverageValueNaive in generation 3 of 10
Model Number: 555 with model ARDL in generation 3 of 10
Model Number: 556 with model MetricMotif in generation 3 of 10
Model Number: 557 with model WindowRegression in generation 3 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 557: WindowRegression
Model Number: 558 with model ARIMA in generation 3 of 10
Model Number: 559 with model MultivariateRegression in generation 3 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.2s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Template Eval Error: ValueError("Model returned NaN due to a preprocessing transformer {'fillna': 'zero', 'transformations': {'0': 'ScipyFilter', '1': 'QuantileTransformer', '2': 'SeasonalDifference', '3': 'SeasonalDifference', '4': 'AlignLastValue'}, 'transformation_params': {'0': {'method': 'butter', 'method_args': {'N': 7, 'btype': 'highpass', 'analog': False, 'output': 'sos', 'Wn': 0.016666666666666666}}, '1': {'output_distribution': 'normal', 'n_quantiles': 85}, '2': {'lag_1': 28, 'method': 'Mean'}, '3': {'lag_1': 420, 'method': 'Mean'}, '4': {'rows': 7, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}}}. fail_on_forecast_nan=True") in model 559: MultivariateRegression
Model Number: 560 with model MultivariateMotif in generation 3 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 561 with model GLS in generation 3 of 10
New Generation: 4 of 10
Model Number: 562 with model LastValueNaive in generation 4 of 10
Model Number: 563 with model UnivariateMotif in generation 4 of 10
Model Number: 564 with model SeasonalNaive in generation 4 of 10
Model Number: 565 with model UnivariateMotif in generation 4 of 10
Template Eval Error: ValueError('Model UnivariateMotif returned NaN for one or more series. fail_on_forecast_nan=True') in model 565: UnivariateMotif
Model Number: 566 with model UnivariateMotif in generation 4 of 10
Model Number: 567 with model Theta in generation 4 of 10
Model Number: 568 with model NVAR in generation 4 of 10
Model Number: 569 with model GLS in generation 4 of 10
Model Number: 570 with model MultivariateRegression in generation 4 of 10
Model Number: 571 with model LastValueNaive in generation 4 of 10
Model Number: 572 with model ARIMA in generation 4 of 10
Model Number: 573 with model NVAR in generation 4 of 10
Model Number: 574 with model Theta in generation 4 of 10
Model Number: 575 with model LastValueNaive in generation 4 of 10
Model Number: 576 with model ETS in generation 4 of 10
Model Number: 577 with model ARDL in generation 4 of 10
Model Number: 578 with model SectionalMotif in generation 4 of 10
Model Number: 579 with model SeasonalNaive in generation 4 of 10
Model Number: 580 with model ARDL in generation 4 of 10
Model Number: 581 with model ConstantNaive in generation 4 of 10
Model Number: 582 with model GLS in generation 4 of 10
Model Number: 583 with model NVAR in generation 4 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 583: NVAR
Model Number: 584 with model GLS in generation 4 of 10
Model Number: 585 with model UnivariateMotif in generation 4 of 10
Model Number: 586 with model SeasonalNaive in generation 4 of 10
Model Number: 587 with model MultivariateRegression in generation 4 of 10
Model Number: 588 with model UnivariateMotif in generation 4 of 10
Model Number: 589 with model MultivariateMotif in generation 4 of 10
Model Number: 590 with model SectionalMotif in generation 4 of 10
Model Number: 591 with model UnobservedComponents in generation 4 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 591: UnobservedComponents
Model Number: 592 with model ARDL in generation 4 of 10
Model Number: 593 with model SectionalMotif in generation 4 of 10
Model Number: 594 with model AverageValueNaive in generation 4 of 10
Model Number: 595 with model AverageValueNaive in generation 4 of 10
Model Number: 596 with model LastValueNaive in generation 4 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 596: LastValueNaive
Model Number: 597 with model DatepartRegression in generation 4 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 598 with model UnobservedComponents in generation 4 of 10
Model Number: 599 with model Theta in generation 4 of 10
Model Number: 600 with model DatepartRegression in generation 4 of 10
Model Number: 601 with model UnivariateMotif in generation 4 of 10
Model Number: 602 with model ARIMA in generation 4 of 10
Model Number: 603 with model ARIMA in generation 4 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 603: ARIMA
Model Number: 604 with model SectionalMotif in generation 4 of 10
Model Number: 605 with model NVAR in generation 4 of 10
Model Number: 606 with model UnivariateMotif in generation 4 of 10
Model Number: 607 with model MetricMotif in generation 4 of 10
Model Number: 608 with model DatepartRegression in generation 4 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 608: DatepartRegression
Model Number: 609 with model UnivariateMotif in generation 4 of 10
Model Number: 610 with model NVAR in generation 4 of 10
Model Number: 611 with model MultivariateRegression in generation 4 of 10
Model Number: 612 with model ARIMA in generation 4 of 10
Model Number: 613 with model NVAR in generation 4 of 10
Model Number: 614 with model NVAR in generation 4 of 10
Model Number: 615 with model SeasonalNaive in generation 4 of 10
Model Number: 616 with model MultivariateRegression in generation 4 of 10
Model Number: 617 with model ARIMA in generation 4 of 10
Model Number: 618 with model ARDL in generation 4 of 10
Model Number: 619 with model Theta in generation 4 of 10
Model Number: 620 with model MultivariateMotif in generation 4 of 10
Model Number: 621 with model ARIMA in generation 4 of 10
Model Number: 622 with model SeasonalNaive in generation 4 of 10
Model Number: 623 with model ETS in generation 4 of 10
Model Number: 624 with model SeasonalNaive in generation 4 of 10
Model Number: 625 with model WindowRegression in generation 4 of 10
Model Number: 626 with model DatepartRegression in generation 4 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 627 with model Theta in generation 4 of 10
Model Number: 628 with model ARDL in generation 4 of 10
Model Number: 629 with model SeasonalNaive in generation 4 of 10
Model Number: 630 with model LastValueNaive in generation 4 of 10
Model Number: 631 with model GLS in generation 4 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 631: GLS
Model Number: 632 with model LastValueNaive in generation 4 of 10
Model Number: 633 with model LastValueNaive in generation 4 of 10
Model Number: 634 with model UnobservedComponents in generation 4 of 10
Model Number: 635 with model NVAR in generation 4 of 10
Model Number: 636 with model MetricMotif in generation 4 of 10
Model Number: 637 with model MetricMotif in generation 4 of 10
Model Number: 638 with model GLS in generation 4 of 10
Model Number: 639 with model UnivariateMotif in generation 4 of 10
Model Number: 640 with model LastValueNaive in generation 4 of 10
Model Number: 641 with model MultivariateRegression in generation 4 of 10
Template Eval Error: ValueError("Input contains NaN, infinity or a value too large for dtype('float64').") in model 641: MultivariateRegression
Model Number: 642 with model NVAR in generation 4 of 10
Model Number: 643 with model DatepartRegression in generation 4 of 10
Model Number: 644 with model AverageValueNaive in generation 4 of 10
Model Number: 645 with model ARDL in generation 4 of 10
Model Number: 646 with model ARDL in generation 4 of 10
Model Number: 647 with model UnivariateMotif in generation 4 of 10
Model Number: 648 with model UnivariateMotif in generation 4 of 10
Model Number: 649 with model MultivariateMotif in generation 4 of 10
Model Number: 650 with model ConstantNaive in generation 4 of 10
Model Number: 651 with model AverageValueNaive in generation 4 of 10
Template Eval Error: ValueError("Model returned NaN due to a preprocessing transformer {'fillna': 'ffill', 'transformations': {'0': 'SeasonalDifference', '1': 'AlignLastValue', '2': 'DifferencedTransformer'}, 'transformation_params': {'0': {'lag_1': 364, 'method': 'Median'}, '1': {'rows': 4, 'lag': 1, 'method': 'multiplicative', 'strength': 1.0, 'first_value_only': False}, '2': {}}}. fail_on_forecast_nan=True") in model 651: AverageValueNaive
Model Number: 652 with model ConstantNaive in generation 4 of 10
Model Number: 653 with model MetricMotif in generation 4 of 10
Model Number: 654 with model ETS in generation 4 of 10
Model Number: 655 with model ARIMA in generation 4 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 655: ARIMA
Model Number: 656 with model LastValueNaive in generation 4 of 10
Model Number: 657 with model ARDL in generation 4 of 10
Model Number: 658 with model ARIMA in generation 4 of 10
Model Number: 659 with model NVAR in generation 4 of 10
Model Number: 660 with model UnivariateMotif in generation 4 of 10
Model Number: 661 with model MultivariateMotif in generation 4 of 10
Model Number: 662 with model SeasonalNaive in generation 4 of 10
Model Number: 663 with model MultivariateMotif in generation 4 of 10
Model Number: 664 with model Theta in generation 4 of 10
Model Number: 665 with model ARIMA in generation 4 of 10
Model Number: 666 with model DatepartRegression in generation 4 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 667 with model ARIMA in generation 4 of 10
Model Number: 668 with model MultivariateMotif in generation 4 of 10
Model Number: 669 with model ARDL in generation 4 of 10
Model Number: 670 with model DatepartRegression in generation 4 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 671 with model AverageValueNaive in generation 4 of 10


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 672 with model SeasonalNaive in generation 4 of 10
Model Number: 673 with model NVAR in generation 4 of 10
Model Number: 674 with model MetricMotif in generation 4 of 10
Model Number: 675 with model ARDL in generation 4 of 10
Model Number: 676 with model NVAR in generation 4 of 10
Model Number: 677 with model UnivariateMotif in generation 4 of 10
Model Number: 678 with model ARDL in generation 4 of 10
Model Number: 679 with model SeasonalNaive in generation 4 of 10
Model Number: 680 with model ARIMA in generation 4 of 10
Model Number: 681 with model MetricMotif in generation 4 of 10
Model Number: 682 with model LastValueNaive in generation 4 of 10
Model Number: 683 with model MultivariateMotif in generation 4 of 10
Model Number: 684 with model MultivariateMotif in generation 4 of 10
Model Number: 685 with model ARIMA in generation 4 of 10
Model Number: 686 with model Theta in generation 4 of 10
New Generation: 5 of 10
Model Number: 687 with model MetricMotif in generation 5 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 687: MetricMotif
Model Number: 688 with model LastValueNaive in generation 5 of 10
Model Number: 689 with model MultivariateRegression in generation 5 of 10
Template Eval Error: ValueError("regression_type='User' but not future_regressor supplied.") in model 689: MultivariateRegression
Model Number: 690 with model AverageValueNaive in generation 5 of 10
Model Number: 691 with model ARDL in generation 5 of 10
Model Number: 692 with model NVAR in generation 5 of 10
Model Number: 693 with model NVAR in generation 5 of 10
Model Number: 694 with model ETS in generation 5 of 10
Model Number: 695 with model ARIMA in generation 5 of 10
Model Number: 696 with model Theta in generation 5 of 10
Model Number: 697 with model LastValueNaive in generation 5 of 10
Model Number: 698 with model GLS in generation 5 of 10
Model Number: 699 with model UnivariateMotif in generation 5 of 10
Model Number: 700 with model UnivariateMotif in generation 5 of 10
Model Number: 701 with model NVAR in generation 5 of 10
Model Number: 702 with model MultivariateMotif in generation 5 of 10
Model Number: 703 with model GLS in generation 5 of 10
Model Number: 704 with model UnivariateMotif in generation 5 of 10
Model Number: 705 with model MultivariateMotif in generation 5 of 10
Model Number: 706 with model ConstantNaive in generation 5 of 10
Model Number: 707 with model ETS in generation 5 of 10
Model Number: 708 with model SeasonalNaive in generation 5 of 10
Model Number: 709 with model ARIMA in generation 5 of 10
Model Number: 710 with model Theta in generation 5 of 10
Model Number: 711 with model MetricMotif in generation 5 of 10
Template Eval Error: TypeError("'NoneType' object is not subscriptable") in model 711: MetricMotif
Model Number: 712 with model ARIMA in generation 5 of 10
Model Number: 713 with model UnivariateMotif in generation 5 of 10
Model Number: 714 with model MultivariateMotif in generation 5 of 10
Model Number: 715 with model MetricMotif in generation 5 of 10
Model Number: 716 with model SeasonalNaive in generation 5 of 10
Model Number: 717 with model UnivariateMotif in generation 5 of 10
Model Number: 718 with model UnobservedComponents in generation 5 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor supplied") in model 718: UnobservedComponents
Model Number: 719 with model UnivariateMotif in generation 5 of 10
Model Number: 720 with model LastValueNaive in generation 5 of 10
Model Number: 721 with model ARIMA in generation 5 of 10
Model Number: 722 with model ETS in generation 5 of 10
Model Number: 723 with model LastValueNaive in generation 5 of 10
Model Number: 724 with model ARIMA in generation 5 of 10
Model Number: 725 with model MetricMotif in generation 5 of 10
Model Number: 726 with model GLS in generation 5 of 10
Model Number: 727 with model ARDL in generation 5 of 10
Model Number: 728 with model SectionalMotif in generation 5 of 10
Model Number: 729 with model SeasonalNaive in generation 5 of 10
Model Number: 730 with model DatepartRegression in generation 5 of 10
Model Number: 731 with model ConstantNaive in generation 5 of 10
Model Number: 732 with model SeasonalNaive in generation 5 of 10
Model Number: 733 with model AverageValueNaive in generation 5 of 10
Model Number: 734 with model LastValueNaive in generation 5 of 10
Model Number: 735 with model MetricMotif in generation 5 of 10
Model Number: 736 with model MultivariateRegression in generation 5 of 10
Model Number: 737 with model AverageValueNaive in generation 5 of 10
Model Number: 738 with model MetricMotif in generation 5 of 10
Model Number: 739 with model MultivariateMotif in generation 5 of 10
Model Number: 740 with model NVAR in generation 5 of 10
Model Number: 741 with model SectionalMotif in generation 5 of 10
Model Number: 742 with model Theta in generation 5 of 10
Model Number: 743 with model LastValueNaive in generation 5 of 10
Model Number: 744 with model ETS in generation 5 of 10
Model Number: 745 with model MetricMotif in generation 5 of 10
Model Number: 746 with model NVAR in generation 5 of 10
Model Number: 747 with model MultivariateMotif in generation 5 of 10
Model Number: 748 with model Theta in generation 5 of 10
Model Number: 749 with model MultivariateMotif in generation 5 of 10
Model Number: 750 with model SeasonalNaive in generation 5 of 10
Model Number: 751 with model ARDL in generation 5 of 10
Model Number: 752 with model NVAR in generation 5 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 752: NVAR
Model Number: 753 with model SectionalMotif in generation 5 of 10
Model Number: 754 with model GLM in generation 5 of 10
Model Number: 755 with model MultivariateRegression in generation 5 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 756 with model UnivariateMotif in generation 5 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 757 with model MultivariateRegression in generation 5 of 10
Model Number: 758 with model SeasonalNaive in generation 5 of 10
Model Number: 759 with model SeasonalNaive in generation 5 of 10
Model Number: 760 with model LastValueNaive in generation 5 of 10
Model Number: 761 with model MultivariateRegression in generation 5 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 762 with model NVAR in generation 5 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 763 with model DatepartRegression in generation 5 of 10
Template Eval Error: AttributeError("module 'tensorflow.compat.v2.__internal__.tracking' has no attribute 'TrackableSaver'") in model 763: DatepartRegression
Model Number: 764 with model SeasonalNaive in generation 5 of 10
Model Number: 765 with model GLS in generation 5 of 10
Model Number: 766 with model GLS in generation 5 of 10
Model Number: 767 with model WindowRegression in generation 5 of 10
Model Number: 768 with model NVAR in generation 5 of 10
Model Number: 769 with model UnivariateMotif in generation 5 of 10
Model Number: 770 with model ARDL in generation 5 of 10
Model Number: 771 with model ARDL in generation 5 of 10
Model Number: 772 with model UnivariateMotif in generation 5 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 772: UnivariateMotif
Model Number: 773 with model SeasonalNaive in generation 5 of 10
Model Number: 774 with model MultivariateRegression in generation 5 of 10
Template Eval Error: ValueError("regression_type='User' but not future_regressor supplied.") in model 774: MultivariateRegression
Model Number: 775 with model SeasonalNaive in generation 5 of 10
Model Number: 776 with model UnivariateMotif in generation 5 of 10
Model Number: 777 with model ConstantNaive in generation 5 of 10
Model Number: 778 with model NVAR in generation 5 of 10
Model Number: 779 with model ARIMA in generation 5 of 10
Model Number: 780 with model ARIMA in generation 5 of 10
Model Number: 781 with model GLM in generation 5 of 10
Template Eval Error: ValueError('The first guess on the deviance function returned a nan.  This could be a boundary  problem and should be reported.') in model 781: GLM
Model Number: 782 with model UnobservedComponents in generation 5 of 10
Model Number: 783 with model MetricMotif in generation 5 of 10
Model Number: 784 with model ARIMA in generation 5 of 10
Model Number: 785 with model MultivariateRegression in generation 5 of 10
Template Eval Error: ValueError("regression_type='User' but not future_regressor supplied.") in model 785: MultivariateRegression
Model Number: 786 with model ARDL in generation 5 of 10
New Generation: 6 of 10
Model Number: 787 with model Theta in generation 6 of 10
Model Number: 788 with model MetricMotif in generation 6 of 10
Model Number: 789 with model ETS in generation 6 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 789: ETS
Model Number: 790 with model ARDL in generation 6 of 10
Model Number: 791 with model GLM in generation 6 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 791: GLM
Model Number: 792 with model UnivariateMotif in generation 6 of 10
Model Number: 793 with model SectionalMotif in generation 6 of 10
Model Number: 794 with model ARIMA in generation 6 of 10
Model Number: 795 with model ARIMA in generation 6 of 10
Model Number: 796 with model SectionalMotif in generation 6 of 10
Model Number: 797 with model SectionalMotif in generation 6 of 10
Model Number: 798 with model DatepartRegression in generation 6 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 798: DatepartRegression
Model Number: 799 with model ARDL in generation 6 of 10
Model Number: 800 with model ARDL in generation 6 of 10
Model Number: 801 with model GLS in generation 6 of 10
Model Number: 802 with model Theta in generation 6 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 802: Theta
Model Number: 803 with model MetricMotif in generation 6 of 10
Model Number: 804 with model Theta in generation 6 of 10
Model Number: 805 with model ARDL in generation 6 of 10
Model Number: 806 with model ARIMA in generation 6 of 10
Model Number: 807 with model ARIMA in generation 6 of 10
Model Number: 808 with model NVAR in generation 6 of 10
Model Number: 809 with model SeasonalNaive in generation 6 of 10
Model Number: 810 with model ARDL in generation 6 of 10
Model Number: 811 with model LastValueNaive in generation 6 of 10
Model Number: 812 with model LastValueNaive in generation 6 of 10
Model Number: 813 with model ARIMA in generation 6 of 10
Model Number: 814 with model UnobservedComponents in generation 6 of 10
Model Number: 815 with model ARDL in generation 6 of 10
Model Number: 816 with model MetricMotif in generation 6 of 10
Model Number: 817 with model UnivariateMotif in generation 6 of 10
Model Number: 818 with model NVAR in generation 6 of 10
Model Number: 819 with model WindowRegression in generation 6 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 819: WindowRegression
Model Number: 820 with model GLS in generation 6 of 10
Model Number: 821 with model LastValueNaive in generation 6 of 10
Model Number: 822 with model ARDL in generation 6 of 10
Model Number: 823 with model SeasonalNaive in generation 6 of 10
Model Number: 824 with model MultivariateRegression in generation 6 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 825 with model NVAR in generation 6 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 826 with model AverageValueNaive in generation 6 of 10
Model Number: 827 with model UnivariateMotif in generation 6 of 10
Model Number: 828 with model MetricMotif in generation 6 of 10
Model Number: 829 with model NVAR in generation 6 of 10
Model Number: 830 with model GLM in generation 6 of 10
Model Number: 831 with model WindowRegression in generation 6 of 10
Template Eval Error: KeyError('model') in model 831: WindowRegression
Model Number: 832 with model MetricMotif in generation 6 of 10
Model Number: 833 with model MetricMotif in generation 6 of 10
Model Number: 834 with model ARIMA in generation 6 of 10
Model Number: 835 with model SeasonalNaive in generation 6 of 10
Model Number: 836 with model MultivariateMotif in generation 6 of 10
Model Number: 837 with model UnivariateMotif in generation 6 of 10
Model Number: 838 with model Theta in generation 6 of 10
Model Number: 839 with model Theta in generation 6 of 10
Model Number: 840 with model LastValueNaive in generation 6 of 10
Model Number: 841 with model ETS in generation 6 of 10
Model Number: 842 with model MultivariateMotif in generation 6 of 10
Model Number: 843 with model SeasonalNaive in generation 6 of 10
Model Number: 844 with model UnobservedComponents in generation 6 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 844: UnobservedComponents
Model Number: 845 with model MultivariateRegression in generation 6 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.6s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.6s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 846 with model NVAR in generation 6 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 847 with model MultivariateMotif in generation 6 of 10
Model Number: 848 with model UnivariateMotif in generation 6 of 10
Model Number: 849 with model UnivariateMotif in generation 6 of 10
Model Number: 850 with model MetricMotif in generation 6 of 10
Model Number: 851 with model ETS in generation 6 of 10
Model Number: 852 with model MultivariateRegression in generation 6 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 853 with model MetricMotif in generation 6 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 854 with model UnivariateMotif in generation 6 of 10
Model Number: 855 with model LastValueNaive in generation 6 of 10
Model Number: 856 with model ARDL in generation 6 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 856: ARDL
Model Number: 857 with model ARDL in generation 6 of 10
Model Number: 858 with model SectionalMotif in generation 6 of 10
Model Number: 859 with model NVAR in generation 6 of 10
Model Number: 860 with model DatepartRegression in generation 6 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 860: DatepartRegression
Model Number: 861 with model NVAR in generation 6 of 10
Model Number: 862 with model ARDL in generation 6 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 862: ARDL
Model Number: 863 with model MultivariateMotif in generation 6 of 10
Model Number: 864 with model NVAR in generation 6 of 10
Model Number: 865 with model AverageValueNaive in generation 6 of 10
Model Number: 866 with model SeasonalNaive in generation 6 of 10
Model Number: 867 with model AverageValueNaive in generation 6 of 10
Model Number: 868 with model NVAR in generation 6 of 10
Model Number: 869 with model AverageValueNaive in generation 6 of 10
Model Number: 870 with model ETS in generation 6 of 10
Model Number: 871 with model NVAR in generation 6 of 10
Model Number: 872 with model WindowRegression in generation 6 of 10
Model Number: 873 with model GLS in generation 6 of 10
Model Number: 874 with model LastValueNaive in generation 6 of 10
Model Number: 875 with model MetricMotif in generation 6 of 10
Model Number: 876 with model MetricMotif in generation 6 of 10
Model Number: 877 with model GLS in generation 6 of 10
Model Number: 878 with model GLS in generation 6 of 10
Model Number: 879 with model SeasonalNaive in generation 6 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 879: SeasonalNaive
Model Number: 880 with model LastValueNaive in generation 6 of 10
Model Number: 881 with model UnobservedComponents in generation 6 of 10
Model Number: 882 with model ARIMA in generation 6 of 10
Model Number: 883 with model ETS in generation 6 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 883: ETS
Model Number: 884 with model SeasonalNaive in generation 6 of 10
Model Number: 885 with model MetricMotif in generation 6 of 10
Model Number: 886 with model ARIMA in generation 6 of 10
New Generation: 7 of 10
Model Number: 887 with model Theta in generation 7 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 887: Theta
Model Number: 888 with model UnobservedComponents in generation 7 of 10
Model Number: 889 with model LastValueNaive in generation 7 of 10
Model Number: 890 with model UnivariateMotif in generation 7 of 10
Model Number: 891 with model ARDL in generation 7 of 10
Model Number: 892 with model SectionalMotif in generation 7 of 10
Model Number: 893 with model MultivariateRegression in generation 7 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 894 with model MetricMotif in generation 7 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 895 with model ConstantNaive in generation 7 of 10
Model Number: 896 with model SeasonalNaive in generation 7 of 10
Model Number: 897 with model MultivariateRegression in generation 7 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 898 with model NVAR in generation 7 of 10
Model Number: 899 with model GLS in generation 7 of 10
Model Number: 900 with model ETS in generation 7 of 10
Model Number: 901 with model ARIMA in generation 7 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 901: ARIMA
Model Number: 902 with model UnobservedComponents in generation 7 of 10
Model Number: 903 with model SeasonalNaive in generation 7 of 10
Model Number: 904 with model UnivariateMotif in generation 7 of 10
Model Number: 905 with model UnivariateMotif in generation 7 of 10
Model Number: 906 with model SeasonalNaive in generation 7 of 10
Model Number: 907 with model SeasonalNaive in generation 7 of 10
Model Number: 908 with model AverageValueNaive in generation 7 of 10
Model Number: 909 with model Theta in generation 7 of 10
Model Number: 910 with model ARDL in generation 7 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 910: ARDL
Model Number: 911 with model MultivariateRegression in generation 7 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 912 with model NVAR in generation 7 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 913 with model MultivariateMotif in generation 7 of 10
Model Number: 914 with model GLS in generation 7 of 10
Model Number: 915 with model NVAR in generation 7 of 10
Model Number: 916 with model UnivariateMotif in generation 7 of 10
Template Eval Error: ValueError('Model UnivariateMotif returned NaN for one or more series. fail_on_forecast_nan=True') in model 916: UnivariateMotif
Model Number: 917 with model SectionalMotif in generation 7 of 10
Model Number: 918 with model NVAR in generation 7 of 10
Model Number: 919 with model ARDL in generation 7 of 10
Model Number: 920 with model LastValueNaive in generation 7 of 10
Model Number: 921 with model ARDL in generation 7 of 10
Model Number: 922 with model LastValueNaive in generation 7 of 10
Model Number: 923 with model MultivariateMotif in generation 7 of 10
Model Number: 924 with model ConstantNaive in generation 7 of 10
Model Number: 925 with model AverageValueNaive in generation 7 of 10
Model Number: 926 with model ARDL in generation 7 of 10
Model Number: 927 with model ARIMA in generation 7 of 10
Model Number: 928 with model LastValueNaive in generation 7 of 10
Model Number: 929 with model Theta in generation 7 of 10
Model Number: 930 with model ARIMA in generation 7 of 10
Model Number: 931 with model AverageValueNaive in generation 7 of 10
Model Number: 932 with model MultivariateRegression in generation 7 of 10
Model Number: 933 with model SeasonalNaive in generation 7 of 10
Model Number: 934 with model MetricMotif in generation 7 of 10
Model Number: 935 with model MetricMotif in generation 7 of 10
Model Number: 936 with model MetricMotif in generation 7 of 10
Model Number: 937 with model ARIMA in generation 7 of 10
Model Number: 938 with model UnivariateMotif in generation 7 of 10
Model Number: 939 with model GLS in generation 7 of 10
Model Number: 940 with model MultivariateMotif in generation 7 of 10
Model Number: 941 with model MultivariateMotif in generation 7 of 10
Model Number: 942 with model MultivariateRegression in generation 7 of 10
Model Number: 943 with model UnobservedComponents in generation 7 of 10
Model Number: 944 with model MetricMotif in generation 7 of 10
Model Number: 945 with model LastValueNaive in generation 7 of 10
Model Number: 946 with model LastValueNaive in generation 7 of 10
Model Number: 947 with model UnivariateMotif in generation 7 of 10
Model Number: 948 with model ARDL in generation 7 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 948: ARDL
Model Number: 949 with model GLS in generation 7 of 10
Model Number: 950 with model NVAR in generation 7 of 10
Model Number: 951 with model NVAR in generation 7 of 10
Model Number: 952 with model GLS in generation 7 of 10
Model Number: 953 with model ARIMA in generation 7 of 10
Model Number: 954 with model ARDL in generation 7 of 10
Model Number: 955 with model MetricMotif in generation 7 of 10
Model Number: 956 with model AverageValueNaive in generation 7 of 10
Model Number: 957 with model GLS in generation 7 of 10
Model Number: 958 with model MultivariateMotif in generation 7 of 10
Model Number: 959 with model Theta in generation 7 of 10
Model Number: 960 with model DatepartRegression in generation 7 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 961 with model MetricMotif in generation 7 of 10


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 962 with model ARDL in generation 7 of 10
Model Number: 963 with model LastValueNaive in generation 7 of 10
Model Number: 964 with model ARIMA in generation 7 of 10
Model Number: 965 with model MetricMotif in generation 7 of 10
Model Number: 966 with model UnivariateMotif in generation 7 of 10
Model Number: 967 with model UnivariateMotif in generation 7 of 10
Model Number: 968 with model NVAR in generation 7 of 10
Model Number: 969 with model ETS in generation 7 of 10
Model Number: 970 with model LastValueNaive in generation 7 of 10
Model Number: 971 with model GLS in generation 7 of 10
Model Number: 972 with model UnivariateMotif in generation 7 of 10
Model Number: 973 with model Theta in generation 7 of 10
Model Number: 974 with model LastValueNaive in generation 7 of 10
Model Number: 975 with model MultivariateRegression in generation 7 of 10
Model Number: 976 with model UnobservedComponents in generation 7 of 10
Model Number: 977 with model LastValueNaive in generation 7 of 10
Model Number: 978 with model ARDL in generation 7 of 10
Model Number: 979 with model ARIMA in generation 7 of 10
Model Number: 980 with model SeasonalNaive in generation 7 of 10
Model Number: 981 with model Theta in generation 7 of 10
Model Number: 982 with model ETS in generation 7 of 10
Model Number: 983 with model AverageValueNaive in generation 7 of 10
Model Number: 984 with model AverageValueNaive in generation 7 of 10
HolidayTransformer: no anomalies detected.
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 984: AverageValueNaive
Model Number: 985 with model UnivariateMotif in generation 7 of 10
Model Number: 986 with model SeasonalNaive in generation 7 of 10
New Generation: 8 of 10
Model Number: 987 with model ETS in generation 8 of 10
Model Number: 988 with model ETS in generation 8 of 10
Model Number: 989 with model MultivariateRegression in generation 8 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 990 with model UnivariateMotif in generation 8 of 10
Model Number: 991 with model ARDL in generation 8 of 10
Model Number: 992 with model GLM in generation 8 of 10
Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 992: GLM
Model Number: 993 with model ARDL in generation 8 of 10
Model Number: 994 with model GLS in generation 8 of 10
Model Number: 995 with model GLS in generation 8 of 10
Model Number: 996 with model LastValueNaive in generation 8 of 10
Model Number: 997 with model ARDL in generation 8 of 10
Model Number: 998 with model SectionalMotif in generation 8 of 10
Model Number: 999 with model LastValueNaive in generation 8 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 999: LastValueNaive
Model Number: 1000 with model ARIMA in generation 8 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1000: ARIMA
Model Number: 1001 with model ARDL in generation 8 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 1001: ARDL
Model Number: 1002 with model MultivariateRegression in generation 8 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 1002: MultivariateRegression
Model Number: 1003 with model SeasonalNaive in generation 8 of 10
Model Number: 1004 with model UnobservedComponents in generation 8 of 10
Model Number: 1005 with model SeasonalNaive in generation 8 of 10
Model Number: 1006 with model UnobservedComponents in generation 8 of 10
Model Number: 1007 with model LastValueNaive in generation 8 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 1007: LastValueNaive
Model Number: 1008 with model UnivariateMotif in generation 8 of 10
Model Number: 1009 with model ARDL in generation 8 of 10
Model Number: 1010 with model UnobservedComponents in generation 8 of 10
Model Number: 1011 with model UnivariateMotif in generation 8 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 1011: UnivariateMotif
Model Number: 1012 with model NVAR in generation 8 of 10
Model Number: 1013 with model AverageValueNaive in generation 8 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1013: AverageValueNaive
Model Number: 1014 with model LastValueNaive in generation 8 of 10
Model Number: 1015 with model NVAR in generation 8 of 10
Model Number: 1016 with model NVAR in generation 8 of 10
Model Number: 1017 with model ARIMA in generation 8 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1017: ARIMA
Model Number: 1018 with model WindowRegression in generation 8 of 10
Template Eval Error: ValueError('Some value(s) of y are out of the valid range for family PoissonDistribution') in model 1018: WindowRegression
Model Number: 1019 with model GLS in generation 8 of 10
Model Number: 1020 with model MultivariateMotif in generation 8 of 10
Model Number: 1021 with model MetricMotif in generation 8 of 10
Model Number: 1022 with model NVAR in generation 8 of 10
Model Number: 1023 with model UnivariateMotif in generation 8 of 10
Model Number: 1024 with model Theta in generation 8 of 10
Model Number: 1025 with model GLS in generation 8 of 10
Model Number: 1026 with model UnivariateMotif in generation 8 of 10
Model Number: 1027 with model WindowRegression in generation 8 of 10
Model Number: 1028 with model MetricMotif in generation 8 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1028: MetricMotif
Model Number: 1029 with model MultivariateRegression in generation 8 of 10
Template Eval Error: ModuleNotFoundError("No module named 'lightgbm'") in model 1029: MultivariateRegression
Model Number: 1030 with model SectionalMotif in generation 8 of 10
Model Number: 1031 with model MultivariateRegression in generation 8 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 1032 with model AverageValueNaive in generation 8 of 10
Model Number: 1033 with model SeasonalNaive in generation 8 of 10
Model Number: 1034 with model AverageValueNaive in generation 8 of 10
Model Number: 1035 with model ARDL in generation 8 of 10
Model Number: 1036 with model ETS in generation 8 of 10
Model Number: 1037 with model UnobservedComponents in generation 8 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor supplied") in model 1037: UnobservedComponents
Model Number: 1038 with model Theta in generation 8 of 10
Model Number: 1039 with model Theta in generation 8 of 10
Model Number: 1040 with model SeasonalNaive in generation 8 of 10
Model Number: 1041 with model ConstantNaive in generation 8 of 10
Model Number: 1042 with model MultivariateMotif in generation 8 of 10
Model Number: 1043 with model NVAR in generation 8 of 10
Model Number: 1044 with model MultivariateMotif in generation 8 of 10
Model Number: 1045 with model ARIMA in generation 8 of 10
Model Number: 1046 with model ARIMA in generation 8 of 10
Model Number: 1047 with model MultivariateMotif in generation 8 of 10
Model Number: 1048 with model Theta in generation 8 of 10
Model Number: 1049 with model SectionalMotif in generation 8 of 10
Model Number: 1050 with model UnivariateMotif in generation 8 of 10
Model Number: 1051 with model MetricMotif in generation 8 of 10
Model Number: 1052 with model MetricMotif in generation 8 of 10
Model Number: 1053 with model ARIMA in generation 8 of 10
Model Number: 1054 with model MultivariateMotif in generation 8 of 10
Model Number: 1055 with model ConstantNaive in generation 8 of 10
Model Number: 1056 with model MultivariateMotif in generation 8 of 10
Model Number: 1057 with model NVAR in generation 8 of 10
Model Number: 1058 with model SeasonalNaive in generation 8 of 10
Model Number: 1059 with model ARIMA in generation 8 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1059: ARIMA
Model Number: 1060 with model ARDL in generation 8 of 10
Model Number: 1061 with model LastValueNaive in generation 8 of 10
Model Number: 1062 with model ARIMA in generation 8 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1062: ARIMA
Model Number: 1063 with model SectionalMotif in generation 8 of 10
Model Number: 1064 with model UnivariateMotif in generation 8 of 10
Model Number: 1065 with model MetricMotif in generation 8 of 10
Model Number: 1066 with model MetricMotif in generation 8 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 1066: MetricMotif
Model Number: 1067 with model NVAR in generation 8 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 1067: NVAR
Model Number: 1068 with model ARIMA in generation 8 of 10
Model Number: 1069 with model AverageValueNaive in generation 8 of 10
Model Number: 1070 with model ETS in generation 8 of 10
Model Number: 1071 with model WindowRegression in generation 8 of 10
Template Eval Error: AttributeError("module 'tensorflow.compat.v2.__internal__.tracking' has no attribute 'TrackableSaver'") in model 1071: WindowRegression
Model Number: 1072 with model MultivariateMotif in generation 8 of 10
Model Number: 1073 with model ARDL in generation 8 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1073: ARDL
Model Number: 1074 with model MetricMotif in generation 8 of 10
Model Number: 1075 with model WindowRegression in generation 8 of 10
Model Number: 1076 with model UnivariateMotif in generation 8 of 10
Model Number: 1077 with model MetricMotif in generation 8 of 10
Model Number: 1078 with model MultivariateRegression in generation 8 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 1078: MultivariateRegression
Model Number: 1079 with model LastValueNaive in generation 8 of 10
Model Number: 1080 with model NVAR in generation 8 of 10
Model Number: 1081 with model ARDL in generation 8 of 10
Model Number: 1082 with model DatepartRegression in generation 8 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 1082: DatepartRegression
Model Number: 1083 with model AverageValueNaive in generation 8 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1083: AverageValueNaive
Model Number: 1084 with model SeasonalNaive in generation 8 of 10
Model Number: 1085 with model ETS in generation 8 of 10
Model Number: 1086 with model NVAR in generation 8 of 10
New Generation: 9 of 10
Model Number: 1087 with model MetricMotif in generation 9 of 10
Model Number: 1088 with model GLS in generation 9 of 10
Model Number: 1089 with model ARIMA in generation 9 of 10
Model Number: 1090 with model LastValueNaive in generation 9 of 10
Model Number: 1091 with model NVAR in generation 9 of 10
Model Number: 1092 with model SeasonalNaive in generation 9 of 10
Model Number: 1093 with model DatepartRegression in generation 9 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


Model Number: 1094 with model ARDL in generation 9 of 10
Model Number: 1095 with model ARDL in generation 9 of 10
Model Number: 1096 with model ARDL in generation 9 of 10
Model Number: 1097 with model ARDL in generation 9 of 10
Model Number: 1098 with model LastValueNaive in generation 9 of 10
Model Number: 1099 with model SeasonalNaive in generation 9 of 10
Model Number: 1100 with model GLS in generation 9 of 10
Model Number: 1101 with model MetricMotif in generation 9 of 10
Model Number: 1102 with model LastValueNaive in generation 9 of 10
Model Number: 1103 with model ARIMA in generation 9 of 10
Model Number: 1104 with model NVAR in generation 9 of 10
Model Number: 1105 with model SeasonalNaive in generation 9 of 10
Model Number: 1106 with model UnivariateMotif in generation 9 of 10
Model Number: 1107 with model LastValueNaive in generation 9 of 10
Model Number: 1108 with model SeasonalNaive in generation 9 of 10
Model Number: 1109 with model ARDL in generation 9 of 10
Model Number: 1110 with model Theta in generation 9 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 1110: Theta
Model Number: 1111 with model Theta in generation 9 of 10
Template Eval Error: Exception('Transformer Cointegration failed on fit') in model 1111: Theta
Model Number: 1112 with model Theta in generation 9 of 10
Model Number: 1113 with model ARIMA in generation 9 of 10
Model Number: 1114 with model MetricMotif in generation 9 of 10
Model Number: 1115 with model MultivariateMotif in generation 9 of 10
Model Number: 1116 with model ConstantNaive in generation 9 of 10
Model Number: 1117 with model UnivariateMotif in generation 9 of 10
Model Number: 1118 with model UnivariateMotif in generation 9 of 10
Model Number: 1119 with model ARDL in generation 9 of 10
HolidayTransformer: no anomalies detected.
Template Eval Error: ValueError("ARDL series Close failed with error MissingDataError('exog contains inf or nans') exog train             weekend  quarter      epoch  month_1  month_2  month_3  month_4  \\\nDate                                                                          \n2022-02-17        0        1  2459627.5        0        1        0        0   \n2022-02-18        0        1  2459628.5        0        1        0        0   \n2022-02-21        0        1  2459631.5        0        1        0        0   \n2022-02-22        0        1  2459632.5        0        1        0        0   \n2022-02-23        0        1  2459633.5        0        1        0        0   \n...             ...      ...        ...      ...      ...      ...      ...   \n2023-02-06        0        1  2459981.5        0        1        0        0   \n2023-02-07        0        1  2459982.5        0        1        0        0   \n2023-02-08        0        1  2459983.5        0        1        0        0   \n2023-02-09        0        1  2459984.5        0        1        0        0   \n2023-02-10        0        1  2459985.5        0        1        0        0   \n\n            month_5  month_6  month_7  ...  month_11  month_12  weekday_0  \\\nDate                                   ...                                  \n2022-02-17        0        0        0  ...         0         0          0   \n2022-02-18        0        0        0  ...         0         0          0   \n2022-02-21        0        0        0  ...         0         0          1   \n2022-02-22        0        0        0  ...         0         0          0   \n2022-02-23        0        0        0  ...         0         0          0   \n...             ...      ...      ...  ...       ...       ...        ...   \n2023-02-06        0        0        0  ...         0         0          1   \n2023-02-07        0        0        0  ...         0         0          0   \n2023-02-08        0        0        0  ...         0         0          0   \n2023-02-09        0        0        0  ...         0         0          0   \n2023-02-10        0        0        0  ...         0         0          0   \n\n            weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \\\nDate                                                                           \n2022-02-17          0          0          1          0          0          0   \n2022-02-18          0          0          0          1          0          0   \n2022-02-21          0          0          0          0          0          0   \n2022-02-22          1          0          0          0          0          0   \n2022-02-23          0          1          0          0          0          0   \n...               ...        ...        ...        ...        ...        ...   \n2023-02-06          0          0          0          0          0          0   \n2023-02-07          1          0          0          0          0          0   \n2023-02-08          0          1          0          0          0          0   \n2023-02-09          0          0          1          0          0          0   \n2023-02-10          0          0          0          1          0          0   \n\n               phase  \nDate                  \n2022-02-17  0.988025  \n2022-02-18  0.953945  \n2022-02-21  0.730206  \n2022-02-22  0.625396  \n2022-02-23  0.513253  \n...              ...  \n2023-02-06  0.991473  \n2023-02-07  0.964759  \n2023-02-08  0.920255  \n2023-02-09  0.858905  \n2023-02-10  0.782226  \n\n[257 rows x 23 columns] and predict             weekend  quarter      epoch  month_1  month_2  month_3  month_4  \\\n2023-02-13        0        1  2459988.5        0        1        0        0   \n2023-02-14        0        1  2459989.5        0        1        0        0   \n2023-02-15        0        1  2459990.5        0        1        0        0   \n2023-02-16        0        1  2459991.5        0        1        0        0   \n2023-02-17        0        1  2459992.5        0        1        0        0   \n\n            month_5  month_6  month_7  ...  month_11  month_12  weekday_0  \\\n2023-02-13        0        0        0  ...         0         0          1   \n2023-02-14        0        0        0  ...         0         0          0   \n2023-02-15        0        0        0  ...         0         0          0   \n2023-02-16        0        0        0  ...         0         0          0   \n2023-02-17        0        0        0  ...         0         0          0   \n\n            weekday_1  weekday_2  weekday_3  weekday_4  weekday_5  weekday_6  \\\n2023-02-13          0          0          0          0          0          0   \n2023-02-14          1          0          0          0          0          0   \n2023-02-15          0          1          0          0          0          0   \n2023-02-16          0          0          1          0          0          0   \n2023-02-17          0          0          0          1          0          0   \n\n               phase  \n2023-02-13  0.484921  \n2023-02-14  0.375421  \n2023-02-15  0.269011  \n2023-02-16  0.172080  \n2023-02-17  0.091510  \n\n[5 rows x 23 columns]") in model 1119: ARDL
Model Number: 1120 with model AverageValueNaive in generation 9 of 10
Model Number: 1121 with model AverageValueNaive in generation 9 of 10
Model Number: 1122 with model Theta in generation 9 of 10
Model Number: 1123 with model WindowRegression in generation 9 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 1123: WindowRegression
Model Number: 1124 with model LastValueNaive in generation 9 of 10
Model Number: 1125 with model ARIMA in generation 9 of 10
Model Number: 1126 with model SeasonalNaive in generation 9 of 10
Model Number: 1127 with model MultivariateRegression in generation 9 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.3s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 1128 with model NVAR in generation 9 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 1128: NVAR
Model Number: 1129 with model SectionalMotif in generation 9 of 10


[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 1130 with model MetricMotif in generation 9 of 10
Model Number: 1131 with model ARDL in generation 9 of 10
Model Number: 1132 with model NVAR in generation 9 of 10
Model Number: 1133 with model SectionalMotif in generation 9 of 10
Model Number: 1134 with model MetricMotif in generation 9 of 10
Model Number: 1135 with model MultivariateRegression in generation 9 of 10
Model Number: 1136 with model AverageValueNaive in generation 9 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 1136: AverageValueNaive
Model Number: 1137 with model ARIMA in generation 9 of 10
Model Number: 1138 with model ETS in generation 9 of 10
Model Number: 1139 with model ARIMA in generation 9 of 10
Model Number: 1140 with model UnobservedComponents in generation 9 of 10
Model Number: 1141 with model LastValueNaive in generation 9 of 10
Model Number: 1142 with model AverageValueNaive in generation 9 of 10
Model Number: 1143 with model MetricMotif in generation 9 of 10
Model Number: 1144 with model ARDL in generation 9 of 10
Template Eval Error: Exception('Transformer DatepartRegression failed on fit') in model 1144: ARDL
Model Number: 1145 with model LastValueNaive in generation 9 of 10
Model Number: 1146 with model WindowRegression in generation 9 of 10
Model Number: 1147 with model AverageValueNaive in generation 9 of 10
Model Number: 1148 with model Theta in generation 9 of 10
Model Number: 1149 with model MultivariateRegression in generation 9 of 10
Template Eval Error: ModuleNotFoundError("No module named 'xgboost'") in model 1149: MultivariateRegression
Model Number: 1150 with model Theta in generation 9 of 10
Model Number: 1151 with model UnobservedComponents in generation 9 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1151: UnobservedComponents
Model Number: 1152 with model MultivariateRegression in generation 9 of 10
Model Number: 1153 with model SectionalMotif in generation 9 of 10
Model Number: 1154 with model GLM in generation 9 of 10
Model Number: 1155 with model WindowRegression in generation 9 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 1155: WindowRegression
Model Number: 1156 with model DatepartRegression in generation 9 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor passed") in model 1156: DatepartRegression
Model Number: 1157 with model AverageValueNaive in generation 9 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1157: AverageValueNaive
Model Number: 1158 with model ARDL in generation 9 of 10
Model Number: 1159 with model MultivariateMotif in generation 9 of 10
Model Number: 1160 with model MultivariateMotif in generation 9 of 10
Model Number: 1161 with model ARIMA in generation 9 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1161: ARIMA
Model Number: 1162 with model NVAR in generation 9 of 10
Model Number: 1163 with model ETS in generation 9 of 10
Model Number: 1164 with model LastValueNaive in generation 9 of 10
Model Number: 1165 with model ETS in generation 9 of 10
Model Number: 1166 with model ARDL in generation 9 of 10
Model Number: 1167 with model Theta in generation 9 of 10
Model Number: 1168 with model ARIMA in generation 9 of 10
Model Number: 1169 with model SeasonalNaive in generation 9 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1169: SeasonalNaive
Model Number: 1170 with model SeasonalNaive in generation 9 of 10
Model Number: 1171 with model MultivariateRegression in generation 9 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.3s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s


Model Number: 1172 with model UnivariateMotif in generation 9 of 10


[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 1173 with model GLS in generation 9 of 10
Model Number: 1174 with model UnobservedComponents in generation 9 of 10
Model Number: 1175 with model MetricMotif in generation 9 of 10
Model Number: 1176 with model NVAR in generation 9 of 10
Model Number: 1177 with model UnivariateMotif in generation 9 of 10
Model Number: 1178 with model SeasonalNaive in generation 9 of 10
Template Eval Error: Exception('Transformer PowerTransformer failed on fit') in model 1178: SeasonalNaive
Model Number: 1179 with model Theta in generation 9 of 10
Model Number: 1180 with model GLS in generation 9 of 10
Model Number: 1181 with model Theta in generation 9 of 10
Model Number: 1182 with model SectionalMotif in generation 9 of 10
Model Number: 1183 with model NVAR in generation 9 of 10
Model Number: 1184 with model Theta in generation 9 of 10
Model Number: 1185 with model ARDL in generation 9 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1185: ARDL
Model Number: 1186 with model UnobservedComponents in generation 9 of 10
New Generation: 10 of 10
Model Number: 1187 with model LastValueNaive in generation 10 of 10
Model Number: 1188 with model ARIMA in generation 10 of 10
Model Number: 1189 with model SeasonalNaive in generation 10 of 10
Model Number: 1190 with model MultivariateMotif in generation 10 of 10
Model Number: 1191 with model LastValueNaive in generation 10 of 10
Model Number: 1192 with model LastValueNaive in generation 10 of 10
Model Number: 1193 with model UnivariateMotif in generation 10 of 10
Model Number: 1194 with model ARDL in generation 10 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1194: ARDL
Model Number: 1195 with model SeasonalNaive in generation 10 of 10
Model Number: 1196 with model NVAR in generation 10 of 10
Template Eval Error: Exception('Transformer Detrend failed on fit') in model 1196: NVAR
Model Number: 1197 with model MultivariateRegression in generation 10 of 10
Template Eval Error: KeyError('model') in model 1197: MultivariateRegression
Model Number: 1198 with model GLS in generation 10 of 10
Model Number: 1199 with model SeasonalNaive in generation 10 of 10
Model Number: 1200 with model AverageValueNaive in generation 10 of 10
Model Number: 1201 with model LastValueNaive in generation 10 of 10
Model Number: 1202 with model UnivariateMotif in generation 10 of 10
Model Number: 1203 with model SectionalMotif in generation 10 of 10
Model Number: 1204 with model MultivariateMotif in generation 10 of 10
Model Number: 1205 with model Theta in generation 10 of 10
Model Number: 1206 with model GLS in generation 10 of 10
Model Number: 1207 with model MetricMotif in generation 10 of 10
Model Number: 1208 with model SeasonalNaive in generation 10 of 10
Model Number: 1209 with model Theta in generation 10 of 10
Model Number: 1210 with model NVAR in generation 10 of 10
Model Number: 1211 with model ARDL in generation 10 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1211: ARDL
Model Number: 1212 with model NVAR in generation 10 of 10
Model Number: 1213 with model SeasonalNaive in generation 10 of 10
Model Number: 1214 with model ARDL in generation 10 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1214: ARDL
Model Number: 1215 with model ARIMA in generation 10 of 10
Model Number: 1216 with model ARIMA in generation 10 of 10
Model Number: 1217 with model LastValueNaive in generation 10 of 10
Model Number: 1218 with model SeasonalNaive in generation 10 of 10
Model Number: 1219 with model MultivariateMotif in generation 10 of 10
Model Number: 1220 with model NVAR in generation 10 of 10
Model Number: 1221 with model ARIMA in generation 10 of 10
Model Number: 1222 with model GLS in generation 10 of 10
Model Number: 1223 with model ARIMA in generation 10 of 10
Model Number: 1224 with model MetricMotif in generation 10 of 10
Model Number: 1225 with model ARDL in generation 10 of 10
Model Number: 1226 with model GLS in generation 10 of 10
Model Number: 1227 with model MetricMotif in generation 10 of 10
Model Number: 1228 with model SeasonalNaive in generation 10 of 10
Model Number: 1229 with model UnivariateMotif in generation 10 of 10
Model Number: 1230 with model MultivariateMotif in generation 10 of 10
Model Number: 1231 with model MultivariateRegression in generation 10 of 10
Model Number: 1232 with model Theta in generation 10 of 10
Model Number: 1233 with model ARIMA in generation 10 of 10
Model Number: 1234 with model Theta in generation 10 of 10
Model Number: 1235 with model ARDL in generation 10 of 10
Model Number: 1236 with model ARIMA in generation 10 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1236: ARIMA
Model Number: 1237 with model MultivariateRegression in generation 10 of 10
Model Number: 1238 with model ARDL in generation 10 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1238: ARDL
Model Number: 1239 with model ARIMA in generation 10 of 10
Model Number: 1240 with model Theta in generation 10 of 10
Model Number: 1241 with model LastValueNaive in generation 10 of 10
Model Number: 1242 with model UnobservedComponents in generation 10 of 10
Template Eval Error: ValueError("regression_type='User' but no future_regressor supplied") in model 1242: UnobservedComponents
Model Number: 1243 with model GLS in generation 10 of 10
Model Number: 1244 with model AverageValueNaive in generation 10 of 10
Model Number: 1245 with model WindowRegression in generation 10 of 10
Model Number: 1246 with model UnivariateMotif in generation 10 of 10
Model Number: 1247 with model Theta in generation 10 of 10
Model Number: 1248 with model SeasonalNaive in generation 10 of 10
Model Number: 1249 with model NVAR in generation 10 of 10
Model Number: 1250 with model ARDL in generation 10 of 10
Model Number: 1251 with model NVAR in generation 10 of 10
Model Number: 1252 with model ETS in generation 10 of 10
Model Number: 1253 with model MultivariateRegression in generation 10 of 10
Model Number: 1254 with model ARDL in generation 10 of 10
Template Eval Error: ValueError("regression_type='User' but future_regressor not supplied") in model 1254: ARDL
Model Number: 1255 with model MultivariateRegression in generation 10 of 10


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


Model Number: 1256 with model NVAR in generation 10 of 10
Template Eval Error: Exception('Transformer BTCD failed on fit') in model 1256: NVAR
Model Number: 1257 with model MetricMotif in generation 10 of 10
Model Number: 1258 with model LastValueNaive in generation 10 of 10
Model Number: 1259 with model MetricMotif in generation 10 of 10
Model Number: 1260 with model Theta in generation 10 of 10
Model Number: 1261 with model SectionalMotif in generation 10 of 10
Model Number: 1262 with model Ensemble in generation 11 of Ensembles
Model Number: 1263 with model Ensemble in generation 11 of Ensembles
Model Number: 1264 with model Ensemble in generation 11 of Ensembles
Model Number: 1265 with model Ensemble in generation 11 of Ensembles
Model Number: 1266 with model Ensemble in generation 11 of Ensembles
Model Number: 1267 with model Ensemble in generation 11 of Ensembles
Model Number: 1268 with model Ensemble in generation 11 of Ensembles
Model Number: 1269 with model Ensemble in generation 11 of Ensembles
Validation Round: 1
Model Number: 1 of 176 with model Ensemble for Validation 1
📈 1 - Ensemble with avg smape 0.8: 
Model Number: 2 of 176 with model Ensemble for Validation 1
2 - Ensemble with avg smape 0.82: 
Model Number: 3 of 176 with model NVAR for Validation 1
3 - NVAR with avg smape 0.83: 
Model Number: 4 of 176 with model Ensemble for Validation 1
4 - Ensemble with avg smape 0.84: 
Model Number: 5 of 176 with model Ensemble for Validation 1
5 - Ensemble with avg smape 0.85: 
Model Number: 6 of 176 with model Ensemble for Validation 1
6 - Ensemble with avg smape 0.82: 
Model Number: 7 of 176 with model Theta for Validation 1
7 - Theta with avg smape 0.82: 
Model Number: 8 of 176 with model Ensemble for Validation 1
8 - Ensemble with avg smape 0.82: 
Model Number: 9 of 176 with model Theta for Validation 1
9 - Theta with avg smape 0.82: 
Model Number: 10 of 176 with model Theta for Validation 1
10 - Theta with avg smape 0.82: 
Model Number: 11 of 176 with model ARDL for Validation 1
📈 11 - ARDL with avg smape 0.76: 
Model Number: 12 of 176 with model ARDL for Validation 1
12 - ARDL with avg smape 0.76: 
Model Number: 13 of 176 with model MetricMotif for Validation 1
13 - MetricMotif with avg smape 0.79: 
Model Number: 14 of 176 with model ARDL for Validation 1
14 - ARDL with avg smape 1.83: 
Model Number: 15 of 176 with model MetricMotif for Validation 1
15 - MetricMotif with avg smape 1.31: 
Model Number: 16 of 176 with model NVAR for Validation 1
16 - NVAR with avg smape 1.77: 
Model Number: 17 of 176 with model ARIMA for Validation 1
17 - ARIMA with avg smape 0.89: 
Model Number: 18 of 176 with model NVAR for Validation 1
18 - NVAR with avg smape 1.76: 
Model Number: 19 of 176 with model MultivariateMotif for Validation 1
19 - MultivariateMotif with avg smape 3.5: 
Model Number: 20 of 176 with model NVAR for Validation 1
20 - NVAR with avg smape 1.77: 
Model Number: 21 of 176 with model NVAR for Validation 1
21 - NVAR with avg smape 1.79: 
Model Number: 22 of 176 with model NVAR for Validation 1
22 - NVAR with avg smape 1.77: 
Model Number: 23 of 176 with model NVAR for Validation 1
23 - NVAR with avg smape 1.77: 
Model Number: 24 of 176 with model NVAR for Validation 1
24 - NVAR with avg smape 1.77: 
Model Number: 25 of 176 with model NVAR for Validation 1
25 - NVAR with avg smape 1.77: 
Model Number: 26 of 176 with model WindowRegression for Validation 1
26 - WindowRegression with avg smape 0.9: 
Model Number: 27 of 176 with model MetricMotif for Validation 1
27 - MetricMotif with avg smape 1.73: 
Model Number: 28 of 176 with model LastValueNaive for Validation 1
28 - LastValueNaive with avg smape 0.81: 
Model Number: 29 of 176 with model LastValueNaive for Validation 1
29 - LastValueNaive with avg smape 0.81: 
Model Number: 30 of 176 with model LastValueNaive for Validation 1
30 - LastValueNaive with avg smape 2.42: 
Model Number: 31 of 176 with model LastValueNaive for Validation 1
31 - LastValueNaive with avg smape 2.42: 
Model Number: 32 of 176 with model LastValueNaive for Validation 1
32 - LastValueNaive with avg smape 2.42: 
Model Number: 33 of 176 with model LastValueNaive for Validation 1
33 - LastValueNaive with avg smape 0.82: 
Model Number: 34 of 176 with model LastValueNaive for Validation 1
34 - LastValueNaive with avg smape 0.82: 
Model Number: 35 of 176 with model LastValueNaive for Validation 1
35 - LastValueNaive with avg smape 2.4: 
Model Number: 36 of 176 with model LastValueNaive for Validation 1
36 - LastValueNaive with avg smape 0.83: 
Model Number: 37 of 176 with model MetricMotif for Validation 1
37 - MetricMotif with avg smape 1.26: 
Model Number: 38 of 176 with model SeasonalNaive for Validation 1
38 - SeasonalNaive with avg smape 0.79: 
Model Number: 39 of 176 with model ARDL for Validation 1
39 - ARDL with avg smape 0.84: 
Model Number: 40 of 176 with model MultivariateRegression for Validation 1
40 - MultivariateRegression with avg smape 4.82: 
Model Number: 41 of 176 with model ARDL for Validation 1
41 - ARDL with avg smape 2.27: 
Model Number: 42 of 176 with model UnobservedComponents for Validation 1
42 - UnobservedComponents with avg smape 2.61: 
Model Number: 43 of 176 with model Ensemble for Validation 1
43 - Ensemble with avg smape 1.28: 
Model Number: 44 of 176 with model ARDL for Validation 1
44 - ARDL with avg smape 1.55: 
Model Number: 45 of 176 with model ARIMA for Validation 1
📈 45 - ARIMA with avg smape 0.72: 
Model Number: 46 of 176 with model SeasonalNaive for Validation 1
46 - SeasonalNaive with avg smape 1.47: 
Model Number: 47 of 176 with model Ensemble for Validation 1
47 - Ensemble with avg smape 1.34: 
Model Number: 48 of 176 with model ARDL for Validation 1
48 - ARDL with avg smape 1.36: 
Model Number: 49 of 176 with model Theta for Validation 1
49 - Theta with avg smape 1.82: 
Model Number: 50 of 176 with model MultivariateMotif for Validation 1
50 - MultivariateMotif with avg smape 0.76: 
Model Number: 51 of 176 with model Theta for Validation 1
51 - Theta with avg smape 0.74: 
Model Number: 52 of 176 with model SeasonalNaive for Validation 1
52 - SeasonalNaive with avg smape 1.58: 
Model Number: 53 of 176 with model MultivariateMotif for Validation 1
53 - MultivariateMotif with avg smape 2.18: 
Model Number: 54 of 176 with model MultivariateMotif for Validation 1
54 - MultivariateMotif with avg smape 2.18: 
Model Number: 55 of 176 with model SeasonalNaive for Validation 1
55 - SeasonalNaive with avg smape 1.62: 
Model Number: 56 of 176 with model MultivariateRegression for Validation 1
56 - MultivariateRegression with avg smape 0.82: 
Model Number: 57 of 176 with model Theta for Validation 1
57 - Theta with avg smape 0.74: 
Model Number: 58 of 176 with model SeasonalNaive for Validation 1
58 - SeasonalNaive with avg smape 1.59: 
Model Number: 59 of 176 with model SeasonalNaive for Validation 1
59 - SeasonalNaive with avg smape 1.57: 
Model Number: 60 of 176 with model SeasonalNaive for Validation 1
60 - SeasonalNaive with avg smape 1.12: 
Model Number: 61 of 176 with model SeasonalNaive for Validation 1
61 - SeasonalNaive with avg smape 1.6: 
Model Number: 62 of 176 with model SeasonalNaive for Validation 1
62 - SeasonalNaive with avg smape 1.58: 
Model Number: 63 of 176 with model WindowRegression for Validation 1
63 - WindowRegression with avg smape 0.84: 
Model Number: 64 of 176 with model ARDL for Validation 1
64 - ARDL with avg smape 1.59: 
Model Number: 65 of 176 with model MultivariateMotif for Validation 1
65 - MultivariateMotif with avg smape 2.22: 
Model Number: 66 of 176 with model MetricMotif for Validation 1
66 - MetricMotif with avg smape 1.97: 
Model Number: 67 of 176 with model ARDL for Validation 1
67 - ARDL with avg smape 1.59: 
Model Number: 68 of 176 with model MetricMotif for Validation 1
68 - MetricMotif with avg smape 3.93: 
Model Number: 69 of 176 with model AverageValueNaive for Validation 1
69 - AverageValueNaive with avg smape 0.77: 
Model Number: 70 of 176 with model ARIMA for Validation 1
70 - ARIMA with avg smape 1.03: 
Model Number: 71 of 176 with model GLM for Validation 1
71 - GLM with avg smape 1.24: 
Model Number: 72 of 176 with model Theta for Validation 1
72 - Theta with avg smape 1.88: 
Model Number: 73 of 176 with model ARIMA for Validation 1
73 - ARIMA with avg smape 0.76: 
Model Number: 74 of 176 with model MetricMotif for Validation 1
74 - MetricMotif with avg smape 1.16: 
Model Number: 75 of 176 with model MultivariateMotif for Validation 1
📈 75 - MultivariateMotif with avg smape 0.71: 
Model Number: 76 of 176 with model ARIMA for Validation 1
76 - ARIMA with avg smape 0.84: 
Model Number: 77 of 176 with model MetricMotif for Validation 1
77 - MetricMotif with avg smape 2.44: 
Model Number: 78 of 176 with model MetricMotif for Validation 1
78 - MetricMotif with avg smape 3.52: 
Model Number: 79 of 176 with model GLS for Validation 1
79 - GLS with avg smape 1.3: 
Model Number: 80 of 176 with model UnobservedComponents for Validation 1
80 - UnobservedComponents with avg smape 0.82: 
Model Number: 81 of 176 with model ARIMA for Validation 1
81 - ARIMA with avg smape 0.77: 
Model Number: 82 of 176 with model MultivariateMotif for Validation 1
📈 82 - MultivariateMotif with avg smape 0.7: 
Model Number: 83 of 176 with model MultivariateRegression for Validation 1
83 - MultivariateRegression with avg smape 4.29: 
Model Number: 84 of 176 with model UnivariateMotif for Validation 1
84 - UnivariateMotif with avg smape 2.39: 
Model Number: 85 of 176 with model MultivariateRegression for Validation 1
85 - MultivariateRegression with avg smape 2.34: 
Model Number: 86 of 176 with model Theta for Validation 1
86 - Theta with avg smape 1.62: 
Model Number: 87 of 176 with model UnivariateMotif for Validation 1
87 - UnivariateMotif with avg smape 2.04: 
Model Number: 88 of 176 with model MultivariateRegression for Validation 1
88 - MultivariateRegression with avg smape 0.86: 
Model Number: 89 of 176 with model MultivariateMotif for Validation 1
89 - MultivariateMotif with avg smape 0.9: 
Model Number: 90 of 176 with model MultivariateRegression for Validation 1
90 - MultivariateRegression with avg smape 0.86: 
Model Number: 91 of 176 with model GLS for Validation 1
91 - GLS with avg smape 1.24: 
Model Number: 92 of 176 with model MultivariateMotif for Validation 1
92 - MultivariateMotif with avg smape 1.44: 
Model Number: 93 of 176 with model Theta for Validation 1
93 - Theta with avg smape 2.55: 
Model Number: 94 of 176 with model ARIMA for Validation 1
94 - ARIMA with avg smape 0.74: 
Model Number: 95 of 176 with model MultivariateRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


95 - MultivariateRegression with avg smape 2.28: 
Model Number: 96 of 176 with model MultivariateRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.3s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.3s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


96 - MultivariateRegression with avg smape 0.7: 
Model Number: 97 of 176 with model MultivariateRegression for Validation 1
97 - MultivariateRegression with avg smape 0.82: 
Model Number: 98 of 176 with model UnivariateMotif for Validation 1
98 - UnivariateMotif with avg smape 2.33: 
Model Number: 99 of 176 with model UnivariateMotif for Validation 1
99 - UnivariateMotif with avg smape 3.32: 
Model Number: 100 of 176 with model ARIMA for Validation 1
100 - ARIMA with avg smape 0.76: 
Model Number: 101 of 176 with model ARIMA for Validation 1
101 - ARIMA with avg smape 0.75: 
Model Number: 102 of 176 with model GLS for Validation 1
102 - GLS with avg smape 1.24: 
Model Number: 103 of 176 with model GLS for Validation 1
103 - GLS with avg smape 1.24: 
Model Number: 104 of 176 with model GLS for Validation 1
104 - GLS with avg smape 1.59: 
Model Number: 105 of 176 with model AverageValueNaive for Validation 1
105 - AverageValueNaive with avg smape 0.88: 
Model Number: 106 of 176 with model AverageValueNaive for Validation 1
106 - AverageValueNaive with avg smape 0.88: 
Model Number: 107 of 176 with model AverageValueNaive for Validation 1
107 - AverageValueNaive with avg smape 0.88: 
Model Number: 108 of 176 with model AverageValueNaive for Validation 1
108 - AverageValueNaive with avg smape 0.88: 
Model Number: 109 of 176 with model GLS for Validation 1
109 - GLS with avg smape 1.24: 
Model Number: 110 of 176 with model AverageValueNaive for Validation 1
110 - AverageValueNaive with avg smape 0.94: 
Model Number: 111 of 176 with model AverageValueNaive for Validation 1
111 - AverageValueNaive with avg smape 0.94: 
Model Number: 112 of 176 with model AverageValueNaive for Validation 1
112 - AverageValueNaive with avg smape 0.94: 
Model Number: 113 of 176 with model UnivariateMotif for Validation 1
113 - UnivariateMotif with avg smape 1.45: 
Model Number: 114 of 176 with model AverageValueNaive for Validation 1
114 - AverageValueNaive with avg smape 0.93: 
Model Number: 115 of 176 with model UnivariateMotif for Validation 1
115 - UnivariateMotif with avg smape 2.29: 
Model Number: 116 of 176 with model UnivariateMotif for Validation 1
116 - UnivariateMotif with avg smape 4.49: 
Model Number: 117 of 176 with model UnivariateMotif for Validation 1
117 - UnivariateMotif with avg smape 4.49: 
Model Number: 118 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


118 - DatepartRegression with avg smape 0.83: 
Model Number: 119 of 176 with model UnivariateMotif for Validation 1


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


119 - UnivariateMotif with avg smape 2.41: 
Model Number: 120 of 176 with model WindowRegression for Validation 1
120 - WindowRegression with avg smape 3.43: 
Model Number: 121 of 176 with model SectionalMotif for Validation 1
121 - SectionalMotif with avg smape 1.9: 
Model Number: 122 of 176 with model UnobservedComponents for Validation 1
122 - UnobservedComponents with avg smape 2.41: 
Model Number: 123 of 176 with model UnobservedComponents for Validation 1
123 - UnobservedComponents with avg smape 2.41: 
Model Number: 124 of 176 with model ETS for Validation 1
124 - ETS with avg smape 0.9: 
Model Number: 125 of 176 with model GLS for Validation 1
125 - GLS with avg smape 1.89: 
Model Number: 126 of 176 with model SectionalMotif for Validation 1
126 - SectionalMotif with avg smape 1.56: 
Model Number: 127 of 176 with model GLS for Validation 1
127 - GLS with avg smape 1.86: 
Model Number: 128 of 176 with model SectionalMotif for Validation 1
128 - SectionalMotif with avg smape 0.85: 
Model Number: 129 of 176 with model GLS for Validation 1
129 - GLS with avg smape 1.29: 
Model Number: 130 of 176 with model ETS for Validation 1
130 - ETS with avg smape 0.99: 
Model Number: 131 of 176 with model SectionalMotif for Validation 1
131 - SectionalMotif with avg smape 1.33: 
Model Number: 132 of 176 with model ETS for Validation 1
132 - ETS with avg smape 2.4: 
Model Number: 133 of 176 with model ETS for Validation 1
133 - ETS with avg smape 2.38: 
Model Number: 134 of 176 with model ETS for Validation 1
134 - ETS with avg smape 2.38: 
Model Number: 135 of 176 with model ETS for Validation 1
135 - ETS with avg smape 1.19: 
Model Number: 136 of 176 with model ETS for Validation 1
136 - ETS with avg smape 1.23: 
Model Number: 137 of 176 with model ETS for Validation 1
137 - ETS with avg smape 1.19: 
Model Number: 138 of 176 with model SectionalMotif for Validation 1
138 - SectionalMotif with avg smape 0.9: 
Model Number: 139 of 176 with model ConstantNaive for Validation 1
139 - ConstantNaive with avg smape 1.03: 
Model Number: 140 of 176 with model WindowRegression for Validation 1
140 - WindowRegression with avg smape 1.21: 
Model Number: 141 of 176 with model WindowRegression for Validation 1
141 - WindowRegression with avg smape 1.44: 
Model Number: 142 of 176 with model ETS for Validation 1
142 - ETS with avg smape 2.26: 
Model Number: 143 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


143 - DatepartRegression with avg smape 2.79: 
Model Number: 144 of 176 with model SectionalMotif for Validation 1


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


144 - SectionalMotif with avg smape 1.89: 
Model Number: 145 of 176 with model UnobservedComponents for Validation 1
145 - UnobservedComponents with avg smape 0.98: 
Model Number: 146 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


146 - DatepartRegression with avg smape 2.76: 
Model Number: 147 of 176 with model SectionalMotif for Validation 1


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


147 - SectionalMotif with avg smape 1.17: 
Model Number: 148 of 176 with model UnobservedComponents for Validation 1
148 - UnobservedComponents with avg smape 3.89: 
Model Number: 149 of 176 with model SectionalMotif for Validation 1
149 - SectionalMotif with avg smape 3.29: 
Model Number: 150 of 176 with model DatepartRegression for Validation 1
150 - DatepartRegression with avg smape 0.77: 
Model Number: 151 of 176 with model UnobservedComponents for Validation 1
151 - UnobservedComponents with avg smape 2.05: 
Model Number: 152 of 176 with model UnobservedComponents for Validation 1
152 - UnobservedComponents with avg smape 0.79: 
Model Number: 153 of 176 with model UnobservedComponents for Validation 1
153 - UnobservedComponents with avg smape 1.46: 
Model Number: 154 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.


154 - DatepartRegression with avg smape 1.23: 
Model Number: 155 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


155 - DatepartRegression with avg smape 1.08: 
Model Number: 156 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.


156 - DatepartRegression with avg smape 2.77: 
Model Number: 157 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


157 - DatepartRegression with avg smape 3.11: 
Model Number: 158 of 176 with model WindowRegression for Validation 1
158 - WindowRegression with avg smape 1.28: 
Model Number: 159 of 176 with model WindowRegression for Validation 1
159 - WindowRegression with avg smape 1.56: 
Model Number: 160 of 176 with model SectionalMotif for Validation 1
160 - SectionalMotif with avg smape 2.72: 
Model Number: 161 of 176 with model WindowRegression for Validation 1
161 - WindowRegression with avg smape 1.63: 
Model Number: 162 of 176 with model DatepartRegression for Validation 1


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


162 - DatepartRegression with avg smape 2.69: 
Model Number: 163 of 176 with model WindowRegression for Validation 1
163 - WindowRegression with avg smape 1.6: 
Model Number: 164 of 176 with model GLM for Validation 1
164 - GLM with avg smape 1.29: 
Model Number: 165 of 176 with model ConstantNaive for Validation 1
165 - ConstantNaive with avg smape 1.27: 
Model Number: 166 of 176 with model GLM for Validation 1
166 - GLM with avg smape 1.51: 
Model Number: 167 of 176 with model ConstantNaive for Validation 1
167 - ConstantNaive with avg smape 1.62: 
Model Number: 168 of 176 with model ConstantNaive for Validation 1
168 - ConstantNaive with avg smape 1.6: 
Model Number: 169 of 176 with model ConstantNaive for Validation 1
169 - ConstantNaive with avg smape 2.23: 
Model Number: 170 of 176 with model GLM for Validation 1
170 - GLM with avg smape 2.82: 
Model Number: 171 of 176 with model ConstantNaive for Validation 1
171 - ConstantNaive with avg smape 2.17: 
Model Number: 172 of 176 with model ConstantNaive for Validation 1
172 - ConstantNaive with avg smape 0.9: 
Model Number: 173 of 176 with model ConstantNaive for Validation 1
173 - ConstantNaive with avg smape 2.36: 
Model Number: 174 of 176 with model ConstantNaive for Validation 1
174 - ConstantNaive with avg smape 2.66: 
Model Number: 175 of 176 with model GLM for Validation 1
175 - GLM with avg smape 1.29: 
Model Number: 176 of 176 with model GLM for Validation 1
176 - GLM with avg smape 1.26: 
Validation Round: 2
Model Number: 1 of 176 with model Ensemble for Validation 2
📈 1 - Ensemble with avg smape 5.86: 
Model Number: 2 of 176 with model Ensemble for Validation 2
2 - Ensemble with avg smape 7.01: 
Model Number: 3 of 176 with model NVAR for Validation 2
3 - NVAR with avg smape 7.1: 
Model Number: 4 of 176 with model Ensemble for Validation 2
4 - Ensemble with avg smape 6.99: 
Model Number: 5 of 176 with model Ensemble for Validation 2
5 - Ensemble with avg smape 6.99: 
Model Number: 6 of 176 with model Ensemble for Validation 2
6 - Ensemble with avg smape 6.96: 
Model Number: 7 of 176 with model Theta for Validation 2
7 - Theta with avg smape 6.96: 
Model Number: 8 of 176 with model Ensemble for Validation 2
8 - Ensemble with avg smape 6.1: 
Model Number: 9 of 176 with model Theta for Validation 2
9 - Theta with avg smape 6.96: 
Model Number: 10 of 176 with model Theta for Validation 2
10 - Theta with avg smape 6.98: 
Model Number: 11 of 176 with model ARDL for Validation 2
📈 11 - ARDL with avg smape 4.39: 
Model Number: 12 of 176 with model ARDL for Validation 2
12 - ARDL with avg smape 4.39: 
Model Number: 13 of 176 with model MetricMotif for Validation 2
13 - MetricMotif with avg smape 4.89: 
Model Number: 14 of 176 with model ARDL for Validation 2
14 - ARDL with avg smape 4.64: 
Model Number: 15 of 176 with model MetricMotif for Validation 2
15 - MetricMotif with avg smape 6.5: 
Model Number: 16 of 176 with model NVAR for Validation 2
📈 16 - NVAR with avg smape 2.23: 
Model Number: 17 of 176 with model ARIMA for Validation 2
17 - ARIMA with avg smape 6.91: 
Model Number: 18 of 176 with model NVAR for Validation 2
18 - NVAR with avg smape 4.55: 
Model Number: 19 of 176 with model MultivariateMotif for Validation 2
📈 19 - MultivariateMotif with avg smape 1.11: 
Model Number: 20 of 176 with model NVAR for Validation 2
20 - NVAR with avg smape 4.54: 
Model Number: 21 of 176 with model NVAR for Validation 2
21 - NVAR with avg smape 4.52: 
Model Number: 22 of 176 with model NVAR for Validation 2
22 - NVAR with avg smape 4.52: 
Model Number: 23 of 176 with model NVAR for Validation 2
23 - NVAR with avg smape 4.52: 
Model Number: 24 of 176 with model NVAR for Validation 2
24 - NVAR with avg smape 4.52: 
Model Number: 25 of 176 with model NVAR for Validation 2
25 - NVAR with avg smape 4.52: 
Model Number: 26 of 176 with model WindowRegression for Validation 2
26 - WindowRegression with avg smape 7.11: 
Model Number: 27 of 176 with model MetricMotif for Validation 2
27 - MetricMotif with avg smape 7.09: 
Model Number: 28 of 176 with model LastValueNaive for Validation 2
28 - LastValueNaive with avg smape 6.91: 
Model Number: 29 of 176 with model LastValueNaive for Validation 2
29 - LastValueNaive with avg smape 6.91: 
Model Number: 30 of 176 with model LastValueNaive for Validation 2
30 - LastValueNaive with avg smape 4.67: 
Model Number: 31 of 176 with model LastValueNaive for Validation 2
31 - LastValueNaive with avg smape 4.67: 
Model Number: 32 of 176 with model LastValueNaive for Validation 2
32 - LastValueNaive with avg smape 4.67: 
Model Number: 33 of 176 with model LastValueNaive for Validation 2
33 - LastValueNaive with avg smape 6.94: 
Model Number: 34 of 176 with model LastValueNaive for Validation 2
34 - LastValueNaive with avg smape 6.94: 
Model Number: 35 of 176 with model LastValueNaive for Validation 2
35 - LastValueNaive with avg smape 4.7: 
Model Number: 36 of 176 with model LastValueNaive for Validation 2
36 - LastValueNaive with avg smape 7.03: 
Model Number: 37 of 176 with model MetricMotif for Validation 2
37 - MetricMotif with avg smape 5.6: 
Model Number: 38 of 176 with model SeasonalNaive for Validation 2
38 - SeasonalNaive with avg smape 6.62: 
Model Number: 39 of 176 with model ARDL for Validation 2
39 - ARDL with avg smape 6.15: 
Model Number: 40 of 176 with model MultivariateRegression for Validation 2
40 - MultivariateRegression with avg smape 7.18: 
Model Number: 41 of 176 with model ARDL for Validation 2
41 - ARDL with avg smape 3.7: 
Model Number: 42 of 176 with model UnobservedComponents for Validation 2
📈 42 - UnobservedComponents with avg smape 1.05: 
Model Number: 43 of 176 with model Ensemble for Validation 2
43 - Ensemble with avg smape 6.99: 
Model Number: 44 of 176 with model ARDL for Validation 2
44 - ARDL with avg smape 7.39: 
Model Number: 45 of 176 with model ARIMA for Validation 2
45 - ARIMA with avg smape 4.95: 
Model Number: 46 of 176 with model SeasonalNaive for Validation 2
📈 46 - SeasonalNaive with avg smape 0.46: 
Model Number: 47 of 176 with model Ensemble for Validation 2
47 - Ensemble with avg smape 6.13: 
Model Number: 48 of 176 with model ARDL for Validation 2
48 - ARDL with avg smape 2.31: 
Model Number: 49 of 176 with model Theta for Validation 2
49 - Theta with avg smape 6.64: 
Model Number: 50 of 176 with model MultivariateMotif for Validation 2
50 - MultivariateMotif with avg smape 4.63: 
Model Number: 51 of 176 with model Theta for Validation 2
51 - Theta with avg smape 6.7: 
Model Number: 52 of 176 with model SeasonalNaive for Validation 2
52 - SeasonalNaive with avg smape 0.48: 
Model Number: 53 of 176 with model MultivariateMotif for Validation 2
53 - MultivariateMotif with avg smape 2.58: 
Model Number: 54 of 176 with model MultivariateMotif for Validation 2
54 - MultivariateMotif with avg smape 2.58: 
Model Number: 55 of 176 with model SeasonalNaive for Validation 2
📈 55 - SeasonalNaive with avg smape 0.43: 
Model Number: 56 of 176 with model MultivariateRegression for Validation 2
56 - MultivariateRegression with avg smape 5.37: 
Model Number: 57 of 176 with model Theta for Validation 2
57 - Theta with avg smape 5.52: 
Model Number: 58 of 176 with model SeasonalNaive for Validation 2
58 - SeasonalNaive with avg smape 0.49: 
Model Number: 59 of 176 with model SeasonalNaive for Validation 2
59 - SeasonalNaive with avg smape 4.77: 
Model Number: 60 of 176 with model SeasonalNaive for Validation 2
60 - SeasonalNaive with avg smape 4.56: 
Model Number: 61 of 176 with model SeasonalNaive for Validation 2
61 - SeasonalNaive with avg smape 4.75: 
Model Number: 62 of 176 with model SeasonalNaive for Validation 2
62 - SeasonalNaive with avg smape 4.77: 
Model Number: 63 of 176 with model WindowRegression for Validation 2
63 - WindowRegression with avg smape 5.29: 
Model Number: 64 of 176 with model ARDL for Validation 2
64 - ARDL with avg smape 2.35: 
Model Number: 65 of 176 with model MultivariateMotif for Validation 2
65 - MultivariateMotif with avg smape 2.7: 
Model Number: 66 of 176 with model MetricMotif for Validation 2
66 - MetricMotif with avg smape 1.1: 
Model Number: 67 of 176 with model ARDL for Validation 2
67 - ARDL with avg smape 2.33: 
Model Number: 68 of 176 with model MetricMotif for Validation 2
68 - MetricMotif with avg smape 9.11: 
Model Number: 69 of 176 with model AverageValueNaive for Validation 2
69 - AverageValueNaive with avg smape 3.82: 
Model Number: 70 of 176 with model ARIMA for Validation 2
70 - ARIMA with avg smape 6.39: 
Model Number: 71 of 176 with model GLM for Validation 2
Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 71: GLM
Model Number: 72 of 176 with model Theta for Validation 2
72 - Theta with avg smape 6.71: 
Model Number: 73 of 176 with model ARIMA for Validation 2
73 - ARIMA with avg smape 8.14: 
Model Number: 74 of 176 with model MetricMotif for Validation 2
74 - MetricMotif with avg smape 1.11: 
Model Number: 75 of 176 with model MultivariateMotif for Validation 2
75 - MultivariateMotif with avg smape 4.56: 
Model Number: 76 of 176 with model ARIMA for Validation 2
76 - ARIMA with avg smape 7.25: 
Model Number: 77 of 176 with model MetricMotif for Validation 2
77 - MetricMotif with avg smape 8.62: 
Model Number: 78 of 176 with model MetricMotif for Validation 2
78 - MetricMotif with avg smape 2.83: 
Model Number: 79 of 176 with model GLS for Validation 2
79 - GLS with avg smape 5.05: 
Model Number: 80 of 176 with model UnobservedComponents for Validation 2
80 - UnobservedComponents with avg smape 6.67: 
Model Number: 81 of 176 with model ARIMA for Validation 2
81 - ARIMA with avg smape 7.97: 
Model Number: 82 of 176 with model MultivariateMotif for Validation 2
82 - MultivariateMotif with avg smape 4.62: 
Model Number: 83 of 176 with model MultivariateRegression for Validation 2
83 - MultivariateRegression with avg smape 6.32: 
Model Number: 84 of 176 with model UnivariateMotif for Validation 2
84 - UnivariateMotif with avg smape 6.23: 
Model Number: 85 of 176 with model MultivariateRegression for Validation 2
85 - MultivariateRegression with avg smape 7.54: 
Model Number: 86 of 176 with model Theta for Validation 2
86 - Theta with avg smape 1.27: 
Model Number: 87 of 176 with model UnivariateMotif for Validation 2
87 - UnivariateMotif with avg smape 8.11: 
Model Number: 88 of 176 with model MultivariateRegression for Validation 2
88 - MultivariateRegression with avg smape 9.36: 
Model Number: 89 of 176 with model MultivariateMotif for Validation 2
89 - MultivariateMotif with avg smape 5.52: 
Model Number: 90 of 176 with model MultivariateRegression for Validation 2
90 - MultivariateRegression with avg smape 7.03: 
Model Number: 91 of 176 with model GLS for Validation 2
91 - GLS with avg smape 4.54: 
Model Number: 92 of 176 with model MultivariateMotif for Validation 2
92 - MultivariateMotif with avg smape 2.15: 
Model Number: 93 of 176 with model Theta for Validation 2
93 - Theta with avg smape 4.56: 
Model Number: 94 of 176 with model ARIMA for Validation 2
94 - ARIMA with avg smape 7.66: 
Model Number: 95 of 176 with model MultivariateRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


95 - MultivariateRegression with avg smape 2.96: 
Model Number: 96 of 176 with model MultivariateRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.2s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.2s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.2s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.2s finished


96 - MultivariateRegression with avg smape 3.24: 
Model Number: 97 of 176 with model MultivariateRegression for Validation 2
97 - MultivariateRegression with avg smape 6.96: 
Model Number: 98 of 176 with model UnivariateMotif for Validation 2
98 - UnivariateMotif with avg smape 5.4: 
Model Number: 99 of 176 with model UnivariateMotif for Validation 2
99 - UnivariateMotif with avg smape 7.34: 
Model Number: 100 of 176 with model ARIMA for Validation 2
100 - ARIMA with avg smape 7.26: 
Model Number: 101 of 176 with model ARIMA for Validation 2
101 - ARIMA with avg smape 7.52: 
Model Number: 102 of 176 with model GLS for Validation 2
102 - GLS with avg smape 3.43: 
Model Number: 103 of 176 with model GLS for Validation 2
103 - GLS with avg smape 5.11: 
Model Number: 104 of 176 with model GLS for Validation 2
104 - GLS with avg smape 4.74: 
Model Number: 105 of 176 with model AverageValueNaive for Validation 2
105 - AverageValueNaive with avg smape 4.45: 
Model Number: 106 of 176 with model AverageValueNaive for Validation 2
106 - AverageValueNaive with avg smape 4.5: 
Model Number: 107 of 176 with model AverageValueNaive for Validation 2
107 - AverageValueNaive with avg smape 4.5: 
Model Number: 108 of 176 with model AverageValueNaive for Validation 2
108 - AverageValueNaive with avg smape 4.5: 
Model Number: 109 of 176 with model GLS for Validation 2
109 - GLS with avg smape 3.82: 
Model Number: 110 of 176 with model AverageValueNaive for Validation 2
110 - AverageValueNaive with avg smape 4.57: 
Model Number: 111 of 176 with model AverageValueNaive for Validation 2
111 - AverageValueNaive with avg smape 4.57: 
Model Number: 112 of 176 with model AverageValueNaive for Validation 2
112 - AverageValueNaive with avg smape 4.62: 
Model Number: 113 of 176 with model UnivariateMotif for Validation 2
113 - UnivariateMotif with avg smape 1.03: 
Model Number: 114 of 176 with model AverageValueNaive for Validation 2
114 - AverageValueNaive with avg smape 4.6: 
Model Number: 115 of 176 with model UnivariateMotif for Validation 2
115 - UnivariateMotif with avg smape 10.75: 
Model Number: 116 of 176 with model UnivariateMotif for Validation 2
116 - UnivariateMotif with avg smape 3.2: 
Model Number: 117 of 176 with model UnivariateMotif for Validation 2
117 - UnivariateMotif with avg smape 3.2: 
Model Number: 118 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


118 - DatepartRegression with avg smape 7.83: 
Model Number: 119 of 176 with model UnivariateMotif for Validation 2


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


119 - UnivariateMotif with avg smape 6.48: 
Model Number: 120 of 176 with model WindowRegression for Validation 2
120 - WindowRegression with avg smape 5.0: 
Model Number: 121 of 176 with model SectionalMotif for Validation 2
121 - SectionalMotif with avg smape 1.69: 
Model Number: 122 of 176 with model UnobservedComponents for Validation 2
122 - UnobservedComponents with avg smape 4.69: 
Model Number: 123 of 176 with model UnobservedComponents for Validation 2
123 - UnobservedComponents with avg smape 4.69: 
Model Number: 124 of 176 with model ETS for Validation 2
124 - ETS with avg smape 6.45: 
Model Number: 125 of 176 with model GLS for Validation 2
125 - GLS with avg smape 5.42: 
Model Number: 126 of 176 with model SectionalMotif for Validation 2
126 - SectionalMotif with avg smape 3.79: 
Model Number: 127 of 176 with model GLS for Validation 2
127 - GLS with avg smape 3.83: 
Model Number: 128 of 176 with model SectionalMotif for Validation 2
128 - SectionalMotif with avg smape 6.08: 
Model Number: 129 of 176 with model GLS for Validation 2
129 - GLS with avg smape 4.24: 
Model Number: 130 of 176 with model ETS for Validation 2
130 - ETS with avg smape 6.12: 
Model Number: 131 of 176 with model SectionalMotif for Validation 2
131 - SectionalMotif with avg smape 4.84: 
Model Number: 132 of 176 with model ETS for Validation 2
132 - ETS with avg smape 4.7: 
Model Number: 133 of 176 with model ETS for Validation 2
133 - ETS with avg smape 8.88: 
Model Number: 134 of 176 with model ETS for Validation 2
134 - ETS with avg smape 8.88: 
Model Number: 135 of 176 with model ETS for Validation 2
135 - ETS with avg smape 5.41: 
Model Number: 136 of 176 with model ETS for Validation 2
136 - ETS with avg smape 5.19: 
Model Number: 137 of 176 with model ETS for Validation 2
137 - ETS with avg smape 2.65: 
Model Number: 138 of 176 with model SectionalMotif for Validation 2
138 - SectionalMotif with avg smape 4.72: 
Model Number: 139 of 176 with model ConstantNaive for Validation 2
139 - ConstantNaive with avg smape 6.4: 
Model Number: 140 of 176 with model WindowRegression for Validation 2
140 - WindowRegression with avg smape 6.56: 
Model Number: 141 of 176 with model WindowRegression for Validation 2
141 - WindowRegression with avg smape 1.83: 
Model Number: 142 of 176 with model ETS for Validation 2
142 - ETS with avg smape 9.11: 
Model Number: 143 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


143 - DatepartRegression with avg smape 4.78: 
Model Number: 144 of 176 with model SectionalMotif for Validation 2
144 - SectionalMotif with avg smape 6.03: 
Model Number: 145 of 176 with model UnobservedComponents for Validation 2
145 - UnobservedComponents with avg smape 6.98: 
Model Number: 146 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.3s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


146 - DatepartRegression with avg smape 8.73: 
Model Number: 147 of 176 with model SectionalMotif for Validation 2
147 - SectionalMotif with avg smape 7.8: 
Model Number: 148 of 176 with model UnobservedComponents for Validation 2
148 - UnobservedComponents with avg smape 8.94: 
Model Number: 149 of 176 with model SectionalMotif for Validation 2
149 - SectionalMotif with avg smape 6.79: 
Model Number: 150 of 176 with model DatepartRegression for Validation 2
150 - DatepartRegression with avg smape 10.86: 
Model Number: 151 of 176 with model UnobservedComponents for Validation 2
151 - UnobservedComponents with avg smape 8.38: 
Model Number: 152 of 176 with model UnobservedComponents for Validation 2
152 - UnobservedComponents with avg smape 6.66: 
Model Number: 153 of 176 with model UnobservedComponents for Validation 2
153 - UnobservedComponents with avg smape 7.31: 
Model Number: 154 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


154 - DatepartRegression with avg smape 7.02: 
Model Number: 155 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.


155 - DatepartRegression with avg smape 5.28: 
Model Number: 156 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.1s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


156 - DatepartRegression with avg smape 2.69: 
Model Number: 157 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


157 - DatepartRegression with avg smape 2.28: 
Model Number: 158 of 176 with model WindowRegression for Validation 2
158 - WindowRegression with avg smape 3.02: 
Model Number: 159 of 176 with model WindowRegression for Validation 2
159 - WindowRegression with avg smape 1.26: 
Model Number: 160 of 176 with model SectionalMotif for Validation 2
160 - SectionalMotif with avg smape 3.13: 
Model Number: 161 of 176 with model WindowRegression for Validation 2
161 - WindowRegression with avg smape 0.74: 
Model Number: 162 of 176 with model DatepartRegression for Validation 2


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.7s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.3s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.3s finished


162 - DatepartRegression with avg smape 3.17: 
Model Number: 163 of 176 with model WindowRegression for Validation 2
163 - WindowRegression with avg smape 6.57: 
Model Number: 164 of 176 with model GLM for Validation 2
164 - GLM with avg smape 0.49: 
Model Number: 165 of 176 with model ConstantNaive for Validation 2
165 - ConstantNaive with avg smape 0.47: 
Model Number: 166 of 176 with model GLM for Validation 2
166 - GLM with avg smape 11.2: 
Model Number: 167 of 176 with model ConstantNaive for Validation 2
167 - ConstantNaive with avg smape 3.04: 
Model Number: 168 of 176 with model ConstantNaive for Validation 2
168 - ConstantNaive with avg smape 7.2: 
Model Number: 169 of 176 with model ConstantNaive for Validation 2
169 - ConstantNaive with avg smape 3.81: 
Model Number: 170 of 176 with model GLM for Validation 2
170 - GLM with avg smape 4.38: 
Model Number: 171 of 176 with model ConstantNaive for Validation 2
171 - ConstantNaive with avg smape 3.48: 
Model Number: 172 of 176 with model ConstantNaive for Validation 2
172 - ConstantNaive with avg smape 4.17: 
Model Number: 173 of 176 with model ConstantNaive for Validation 2
173 - ConstantNaive with avg smape 3.67: 
Model Number: 174 of 176 with model ConstantNaive for Validation 2
174 - ConstantNaive with avg smape 5.83: 
Model Number: 175 of 176 with model GLM for Validation 2
175 - GLM with avg smape 6.38: 
Model Number: 176 of 176 with model GLM for Validation 2
176 - GLM with avg smape 0.47: 
Validation Round: 3
Model Number: 1 of 176 with model Ensemble for Validation 3
📈 1 - Ensemble with avg smape 4.08: 
Model Number: 2 of 176 with model Ensemble for Validation 3
2 - Ensemble with avg smape 4.46: 
Model Number: 3 of 176 with model NVAR for Validation 3
3 - NVAR with avg smape 4.45: 
Model Number: 4 of 176 with model Ensemble for Validation 3
4 - Ensemble with avg smape 4.45: 
Model Number: 5 of 176 with model Ensemble for Validation 3
5 - Ensemble with avg smape 4.45: 
Model Number: 6 of 176 with model Ensemble for Validation 3
6 - Ensemble with avg smape 4.46: 
Model Number: 7 of 176 with model Theta for Validation 3
7 - Theta with avg smape 4.46: 
Model Number: 8 of 176 with model Ensemble for Validation 3
📈 8 - Ensemble with avg smape 4.01: 
Model Number: 9 of 176 with model Theta for Validation 3
9 - Theta with avg smape 4.46: 
Model Number: 10 of 176 with model Theta for Validation 3
10 - Theta with avg smape 4.49: 
Model Number: 11 of 176 with model ARDL for Validation 3
📈 11 - ARDL with avg smape 3.32: 
Model Number: 12 of 176 with model ARDL for Validation 3
📈 12 - ARDL with avg smape 3.31: 
Model Number: 13 of 176 with model MetricMotif for Validation 3
13 - MetricMotif with avg smape 3.57: 
Model Number: 14 of 176 with model ARDL for Validation 3
14 - ARDL with avg smape 4.04: 
Model Number: 15 of 176 with model MetricMotif for Validation 3
📈 15 - MetricMotif with avg smape 2.66: 
Model Number: 16 of 176 with model NVAR for Validation 3
16 - NVAR with avg smape 3.46: 
Model Number: 17 of 176 with model ARIMA for Validation 3
17 - ARIMA with avg smape 4.42: 
Model Number: 18 of 176 with model NVAR for Validation 3
18 - NVAR with avg smape 3.45: 
Model Number: 19 of 176 with model MultivariateMotif for Validation 3
📈 19 - MultivariateMotif with avg smape 1.56: 
Model Number: 20 of 176 with model NVAR for Validation 3
20 - NVAR with avg smape 3.45: 
Model Number: 21 of 176 with model NVAR for Validation 3
21 - NVAR with avg smape 3.44: 
Model Number: 22 of 176 with model NVAR for Validation 3
22 - NVAR with avg smape 3.44: 
Model Number: 23 of 176 with model NVAR for Validation 3
23 - NVAR with avg smape 3.44: 
Model Number: 24 of 176 with model NVAR for Validation 3
24 - NVAR with avg smape 3.44: 
Model Number: 25 of 176 with model NVAR for Validation 3
25 - NVAR with avg smape 3.44: 
Model Number: 26 of 176 with model WindowRegression for Validation 3
26 - WindowRegression with avg smape 4.48: 
Model Number: 27 of 176 with model MetricMotif for Validation 3
27 - MetricMotif with avg smape 2.58: 
Model Number: 28 of 176 with model LastValueNaive for Validation 3
28 - LastValueNaive with avg smape 4.42: 
Model Number: 29 of 176 with model LastValueNaive for Validation 3
29 - LastValueNaive with avg smape 4.42: 
Model Number: 30 of 176 with model LastValueNaive for Validation 3
30 - LastValueNaive with avg smape 4.15: 
Model Number: 31 of 176 with model LastValueNaive for Validation 3
31 - LastValueNaive with avg smape 4.15: 
Model Number: 32 of 176 with model LastValueNaive for Validation 3
32 - LastValueNaive with avg smape 4.15: 
Model Number: 33 of 176 with model LastValueNaive for Validation 3
33 - LastValueNaive with avg smape 4.44: 
Model Number: 34 of 176 with model LastValueNaive for Validation 3
34 - LastValueNaive with avg smape 4.44: 
Model Number: 35 of 176 with model LastValueNaive for Validation 3
35 - LastValueNaive with avg smape 4.18: 
Model Number: 36 of 176 with model LastValueNaive for Validation 3
36 - LastValueNaive with avg smape 4.54: 
Model Number: 37 of 176 with model MetricMotif for Validation 3
37 - MetricMotif with avg smape 2.41: 
Model Number: 38 of 176 with model SeasonalNaive for Validation 3
38 - SeasonalNaive with avg smape 4.93: 
Model Number: 39 of 176 with model ARDL for Validation 3
39 - ARDL with avg smape 6.17: 
Model Number: 40 of 176 with model MultivariateRegression for Validation 3
40 - MultivariateRegression with avg smape 4.02: 
Model Number: 41 of 176 with model ARDL for Validation 3
41 - ARDL with avg smape 3.8: 
Model Number: 42 of 176 with model UnobservedComponents for Validation 3
42 - UnobservedComponents with avg smape 3.32: 
Model Number: 43 of 176 with model Ensemble for Validation 3
43 - Ensemble with avg smape 3.27: 
Model Number: 44 of 176 with model ARDL for Validation 3
44 - ARDL with avg smape 5.08: 
Model Number: 45 of 176 with model ARIMA for Validation 3
45 - ARIMA with avg smape 4.36: 
Model Number: 46 of 176 with model SeasonalNaive for Validation 3
46 - SeasonalNaive with avg smape 3.37: 
Model Number: 47 of 176 with model Ensemble for Validation 3
47 - Ensemble with avg smape 3.59: 
Model Number: 48 of 176 with model ARDL for Validation 3
48 - ARDL with avg smape 7.46: 
Model Number: 49 of 176 with model Theta for Validation 3
49 - Theta with avg smape 4.08: 
Model Number: 50 of 176 with model MultivariateMotif for Validation 3
50 - MultivariateMotif with avg smape 2.83: 
Model Number: 51 of 176 with model Theta for Validation 3
51 - Theta with avg smape 4.19: 
Model Number: 52 of 176 with model SeasonalNaive for Validation 3
52 - SeasonalNaive with avg smape 3.29: 
Model Number: 53 of 176 with model MultivariateMotif for Validation 3
53 - MultivariateMotif with avg smape 1.98: 
Model Number: 54 of 176 with model MultivariateMotif for Validation 3
54 - MultivariateMotif with avg smape 1.98: 
Model Number: 55 of 176 with model SeasonalNaive for Validation 3
55 - SeasonalNaive with avg smape 3.24: 
Model Number: 56 of 176 with model MultivariateRegression for Validation 3
56 - MultivariateRegression with avg smape 4.45: 
Model Number: 57 of 176 with model Theta for Validation 3
57 - Theta with avg smape 4.18: 
Model Number: 58 of 176 with model SeasonalNaive for Validation 3
58 - SeasonalNaive with avg smape 3.28: 
Model Number: 59 of 176 with model SeasonalNaive for Validation 3
59 - SeasonalNaive with avg smape 3.25: 
Model Number: 60 of 176 with model SeasonalNaive for Validation 3
60 - SeasonalNaive with avg smape 4.27: 
Model Number: 61 of 176 with model SeasonalNaive for Validation 3
61 - SeasonalNaive with avg smape 3.25: 
Model Number: 62 of 176 with model SeasonalNaive for Validation 3
62 - SeasonalNaive with avg smape 3.24: 
Model Number: 63 of 176 with model WindowRegression for Validation 3
63 - WindowRegression with avg smape 4.06: 
Model Number: 64 of 176 with model ARDL for Validation 3
64 - ARDL with avg smape 7.44: 
Model Number: 65 of 176 with model MultivariateMotif for Validation 3
65 - MultivariateMotif with avg smape 1.75: 
Model Number: 66 of 176 with model MetricMotif for Validation 3
66 - MetricMotif with avg smape 4.35: 
Model Number: 67 of 176 with model ARDL for Validation 3
67 - ARDL with avg smape 7.44: 
Model Number: 68 of 176 with model MetricMotif for Validation 3
68 - MetricMotif with avg smape 5.42: 
Model Number: 69 of 176 with model AverageValueNaive for Validation 3
69 - AverageValueNaive with avg smape 2.18: 
Model Number: 70 of 176 with model ARIMA for Validation 3
70 - ARIMA with avg smape 4.06: 
Model Number: 71 of 176 with model GLM for Validation 3
Template Eval Error: ValueError('NaN, inf or invalid value detected in weights, estimation infeasible.') in model 71: GLM
Model Number: 72 of 176 with model Theta for Validation 3
72 - Theta with avg smape 4.16: 
Model Number: 73 of 176 with model ARIMA for Validation 3
73 - ARIMA with avg smape 3.68: 
Model Number: 74 of 176 with model MetricMotif for Validation 3
74 - MetricMotif with avg smape 5.88: 
Model Number: 75 of 176 with model MultivariateMotif for Validation 3
75 - MultivariateMotif with avg smape 3.41: 
Model Number: 76 of 176 with model ARIMA for Validation 3
76 - ARIMA with avg smape 2.61: 
Model Number: 77 of 176 with model MetricMotif for Validation 3
77 - MetricMotif with avg smape 5.53: 
Model Number: 78 of 176 with model MetricMotif for Validation 3
78 - MetricMotif with avg smape 2.99: 
Model Number: 79 of 176 with model GLS for Validation 3
79 - GLS with avg smape 5.12: 
Model Number: 80 of 176 with model UnobservedComponents for Validation 3
80 - UnobservedComponents with avg smape 3.86: 
Model Number: 81 of 176 with model ARIMA for Validation 3
81 - ARIMA with avg smape 3.41: 
Model Number: 82 of 176 with model MultivariateMotif for Validation 3
82 - MultivariateMotif with avg smape 2.56: 
Model Number: 83 of 176 with model MultivariateRegression for Validation 3
83 - MultivariateRegression with avg smape 5.16: 
Model Number: 84 of 176 with model UnivariateMotif for Validation 3
84 - UnivariateMotif with avg smape 2.14: 
Model Number: 85 of 176 with model MultivariateRegression for Validation 3
85 - MultivariateRegression with avg smape 5.06: 
Model Number: 86 of 176 with model Theta for Validation 3
86 - Theta with avg smape 3.28: 
Model Number: 87 of 176 with model UnivariateMotif for Validation 3
87 - UnivariateMotif with avg smape 3.98: 
Model Number: 88 of 176 with model MultivariateRegression for Validation 3
88 - MultivariateRegression with avg smape 3.55: 
Model Number: 89 of 176 with model MultivariateMotif for Validation 3
89 - MultivariateMotif with avg smape 3.49: 
Model Number: 90 of 176 with model MultivariateRegression for Validation 3
90 - MultivariateRegression with avg smape 3.55: 
Model Number: 91 of 176 with model GLS for Validation 3
91 - GLS with avg smape 4.98: 
Model Number: 92 of 176 with model MultivariateMotif for Validation 3
92 - MultivariateMotif with avg smape 2.04: 
Model Number: 93 of 176 with model Theta for Validation 3
93 - Theta with avg smape 4.05: 
Model Number: 94 of 176 with model ARIMA for Validation 3
94 - ARIMA with avg smape 3.38: 
Model Number: 95 of 176 with model MultivariateRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.5s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.5s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


95 - MultivariateRegression with avg smape 2.98: 
Model Number: 96 of 176 with model MultivariateRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 194 tasks      | elapsed:    0.4s
[Parallel(n_jobs=-2)]: Done 200 out of 200 | elapsed:    0.4s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 194 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 200 out of 200 | elapsed:    0.0s finished


96 - MultivariateRegression with avg smape 3.37: 
Model Number: 97 of 176 with model MultivariateRegression for Validation 3
97 - MultivariateRegression with avg smape 4.46: 
Model Number: 98 of 176 with model UnivariateMotif for Validation 3
98 - UnivariateMotif with avg smape 2.29: 
Model Number: 99 of 176 with model UnivariateMotif for Validation 3
99 - UnivariateMotif with avg smape 3.08: 
Model Number: 100 of 176 with model ARIMA for Validation 3
100 - ARIMA with avg smape 3.79: 
Model Number: 101 of 176 with model ARIMA for Validation 3
101 - ARIMA with avg smape 3.58: 
Model Number: 102 of 176 with model GLS for Validation 3
102 - GLS with avg smape 4.96: 
Model Number: 103 of 176 with model GLS for Validation 3
103 - GLS with avg smape 4.99: 
Model Number: 104 of 176 with model GLS for Validation 3
104 - GLS with avg smape 4.94: 
Model Number: 105 of 176 with model AverageValueNaive for Validation 3
105 - AverageValueNaive with avg smape 5.43: 
Model Number: 106 of 176 with model AverageValueNaive for Validation 3
106 - AverageValueNaive with avg smape 5.44: 
Model Number: 107 of 176 with model AverageValueNaive for Validation 3
107 - AverageValueNaive with avg smape 5.44: 
Model Number: 108 of 176 with model AverageValueNaive for Validation 3
108 - AverageValueNaive with avg smape 5.44: 
Model Number: 109 of 176 with model GLS for Validation 3
109 - GLS with avg smape 4.99: 
Model Number: 110 of 176 with model AverageValueNaive for Validation 3
110 - AverageValueNaive with avg smape 2.19: 
Model Number: 111 of 176 with model AverageValueNaive for Validation 3
111 - AverageValueNaive with avg smape 2.19: 
Model Number: 112 of 176 with model AverageValueNaive for Validation 3
112 - AverageValueNaive with avg smape 2.19: 
Model Number: 113 of 176 with model UnivariateMotif for Validation 3
113 - UnivariateMotif with avg smape 4.97: 
Model Number: 114 of 176 with model AverageValueNaive for Validation 3
114 - AverageValueNaive with avg smape 2.19: 
Model Number: 115 of 176 with model UnivariateMotif for Validation 3
115 - UnivariateMotif with avg smape 4.7: 
Model Number: 116 of 176 with model UnivariateMotif for Validation 3
116 - UnivariateMotif with avg smape 2.6: 
Model Number: 117 of 176 with model UnivariateMotif for Validation 3
117 - UnivariateMotif with avg smape 2.6: 
Model Number: 118 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


118 - DatepartRegression with avg smape 5.5: 
Model Number: 119 of 176 with model UnivariateMotif for Validation 3


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


119 - UnivariateMotif with avg smape 3.82: 
Model Number: 120 of 176 with model WindowRegression for Validation 3
120 - WindowRegression with avg smape 4.77: 
Model Number: 121 of 176 with model SectionalMotif for Validation 3
121 - SectionalMotif with avg smape 4.96: 
Model Number: 122 of 176 with model UnobservedComponents for Validation 3
122 - UnobservedComponents with avg smape 4.17: 
Model Number: 123 of 176 with model UnobservedComponents for Validation 3
123 - UnobservedComponents with avg smape 4.17: 
Model Number: 124 of 176 with model ETS for Validation 3
124 - ETS with avg smape 3.97: 
Model Number: 125 of 176 with model GLS for Validation 3
125 - GLS with avg smape 4.56: 
Model Number: 126 of 176 with model SectionalMotif for Validation 3
126 - SectionalMotif with avg smape 1.99: 
Model Number: 127 of 176 with model GLS for Validation 3
127 - GLS with avg smape 4.56: 
Model Number: 128 of 176 with model SectionalMotif for Validation 3
128 - SectionalMotif with avg smape 3.81: 
Model Number: 129 of 176 with model GLS for Validation 3
129 - GLS with avg smape 4.68: 
Model Number: 130 of 176 with model ETS for Validation 3
130 - ETS with avg smape 3.85: 
Model Number: 131 of 176 with model SectionalMotif for Validation 3
131 - SectionalMotif with avg smape 3.52: 
Model Number: 132 of 176 with model ETS for Validation 3
132 - ETS with avg smape 4.18: 
Model Number: 133 of 176 with model ETS for Validation 3
133 - ETS with avg smape 4.2: 
Model Number: 134 of 176 with model ETS for Validation 3
134 - ETS with avg smape 4.21: 
Model Number: 135 of 176 with model ETS for Validation 3
135 - ETS with avg smape 5.42: 
Model Number: 136 of 176 with model ETS for Validation 3
136 - ETS with avg smape 5.2: 
Model Number: 137 of 176 with model ETS for Validation 3
137 - ETS with avg smape 5.43: 
Model Number: 138 of 176 with model SectionalMotif for Validation 3
138 - SectionalMotif with avg smape 2.92: 
Model Number: 139 of 176 with model ConstantNaive for Validation 3
139 - ConstantNaive with avg smape 4.27: 
Model Number: 140 of 176 with model WindowRegression for Validation 3
140 - WindowRegression with avg smape 4.41: 
Model Number: 141 of 176 with model WindowRegression for Validation 3
141 - WindowRegression with avg smape 2.93: 
Model Number: 142 of 176 with model ETS for Validation 3
142 - ETS with avg smape 4.41: 
Model Number: 143 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


143 - DatepartRegression with avg smape 3.32: 
Model Number: 144 of 176 with model SectionalMotif for Validation 3


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


144 - SectionalMotif with avg smape 2.55: 
Model Number: 145 of 176 with model UnobservedComponents for Validation 3
145 - UnobservedComponents with avg smape 4.28: 
Model Number: 146 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s


146 - DatepartRegression with avg smape 5.23: 
Model Number: 147 of 176 with model SectionalMotif for Validation 3


[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


147 - SectionalMotif with avg smape 4.67: 
Model Number: 148 of 176 with model UnobservedComponents for Validation 3
148 - UnobservedComponents with avg smape 3.99: 
Model Number: 149 of 176 with model SectionalMotif for Validation 3
149 - SectionalMotif with avg smape 3.58: 
Model Number: 150 of 176 with model DatepartRegression for Validation 3
150 - DatepartRegression with avg smape 5.13: 
Model Number: 151 of 176 with model UnobservedComponents for Validation 3
151 - UnobservedComponents with avg smape 3.41: 
Model Number: 152 of 176 with model UnobservedComponents for Validation 3
152 - UnobservedComponents with avg smape 4.15: 
Model Number: 153 of 176 with model UnobservedComponents for Validation 3
153 - UnobservedComponents with avg smape 4.8: 
Model Number: 154 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished
[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.


154 - DatepartRegression with avg smape 4.49: 
Model Number: 155 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


155 - DatepartRegression with avg smape 4.67: 
Model Number: 156 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.1s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


156 - DatepartRegression with avg smape 3.43: 
Model Number: 157 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


157 - DatepartRegression with avg smape 3.06: 
Model Number: 158 of 176 with model WindowRegression for Validation 3
158 - WindowRegression with avg smape 4.24: 
Model Number: 159 of 176 with model WindowRegression for Validation 3
159 - WindowRegression with avg smape 3.42: 
Model Number: 160 of 176 with model SectionalMotif for Validation 3
160 - SectionalMotif with avg smape 3.13: 
Model Number: 161 of 176 with model WindowRegression for Validation 3
161 - WindowRegression with avg smape 4.58: 
Model Number: 162 of 176 with model DatepartRegression for Validation 3


[Parallel(n_jobs=-2)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=-2)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=-2)]: Done 100 out of 100 | elapsed:    0.2s finished
[Parallel(n_jobs=3)]: Using backend ThreadingBackend with 3 concurrent workers.
[Parallel(n_jobs=3)]: Done  44 tasks      | elapsed:    0.0s
[Parallel(n_jobs=3)]: Done 100 out of 100 | elapsed:    0.0s finished


162 - DatepartRegression with avg smape 3.13: 
Model Number: 163 of 176 with model WindowRegression for Validation 3
163 - WindowRegression with avg smape 4.5: 
Model Number: 164 of 176 with model GLM for Validation 3
164 - GLM with avg smape 4.89: 
Model Number: 165 of 176 with model ConstantNaive for Validation 3
165 - ConstantNaive with avg smape 4.92: 
Model Number: 166 of 176 with model GLM for Validation 3
166 - GLM with avg smape 4.36: 
Model Number: 167 of 176 with model ConstantNaive for Validation 3
167 - ConstantNaive with avg smape 2.37: 
Model Number: 168 of 176 with model ConstantNaive for Validation 3
168 - ConstantNaive with avg smape 5.97: 
Model Number: 169 of 176 with model ConstantNaive for Validation 3
169 - ConstantNaive with avg smape 3.26: 
Model Number: 170 of 176 with model GLM for Validation 3
170 - GLM with avg smape 2.99: 
Model Number: 171 of 176 with model ConstantNaive for Validation 3
171 - ConstantNaive with avg smape 2.07: 
Model Number: 172 of 176 with model ConstantNaive for Validation 3
172 - ConstantNaive with avg smape 5.12: 
Model Number: 173 of 176 with model ConstantNaive for Validation 3
173 - ConstantNaive with avg smape 2.01: 
Model Number: 174 of 176 with model ConstantNaive for Validation 3
174 - ConstantNaive with avg smape 2.36: 
Model Number: 175 of 176 with model GLM for Validation 3
175 - GLM with avg smape 5.79: 
Model Number: 176 of 176 with model GLM for Validation 3
176 - GLM with avg smape 4.94: 
Model Number: 1798 with model Ensemble in generation 12 of Ensembles
Model Number: 1799 with model Ensemble in generation 12 of Ensembles
Model Number: 1800 with model Ensemble in generation 12 of Ensembles
Model Number: 1801 with model Ensemble in generation 12 of Ensembles
Model Number: 1802 with model Ensemble in generation 12 of Ensembles
Model Number: 1803 with model Ensemble in generation 12 of Ensembles
Model Number: 1804 with model Ensemble in generation 12 of Ensembles
Model Number: 1805 with model Ensemble in generation 12 of Ensembles
Validation Round: 1
Model Number: 1 of 8 with model Ensemble for Validation 1
📈 1 - Ensemble with avg smape 1.59: 
Model Number: 2 of 8 with model Ensemble for Validation 1
📈 2 - Ensemble with avg smape 1.57: 
Model Number: 3 of 8 with model Ensemble for Validation 1
3 - Ensemble with avg smape 1.59: 
Model Number: 4 of 8 with model Ensemble for Validation 1
4 - Ensemble with avg smape 1.59: 
Model Number: 5 of 8 with model Ensemble for Validation 1
📈 5 - Ensemble with avg smape 1.12: 
Model Number: 6 of 8 with model Ensemble for Validation 1
6 - Ensemble with avg smape 1.53: 
Model Number: 7 of 8 with model Ensemble for Validation 1
7 - Ensemble with avg smape 1.14: 
Model Number: 8 of 8 with model Ensemble for Validation 1
8 - Ensemble with avg smape 1.12: 
Validation Round: 2
Model Number: 1 of 8 with model Ensemble for Validation 2
📈 1 - Ensemble with avg smape 0.47: 
Model Number: 2 of 8 with model Ensemble for Validation 2
📈 2 - Ensemble with avg smape 0.46: 
Model Number: 3 of 8 with model Ensemble for Validation 2
3 - Ensemble with avg smape 0.47: 
Model Number: 4 of 8 with model Ensemble for Validation 2
4 - Ensemble with avg smape 0.47: 
Model Number: 5 of 8 with model Ensemble for Validation 2
5 - Ensemble with avg smape 0.75: 
Model Number: 6 of 8 with model Ensemble for Validation 2
6 - Ensemble with avg smape 0.6: 
Model Number: 7 of 8 with model Ensemble for Validation 2
7 - Ensemble with avg smape 0.73: 
Model Number: 8 of 8 with model Ensemble for Validation 2
8 - Ensemble with avg smape 0.75: 
Validation Round: 3
Model Number: 1 of 8 with model Ensemble for Validation 3
📈 1 - Ensemble with avg smape 3.27: 
Model Number: 2 of 8 with model Ensemble for Validation 3
2 - Ensemble with avg smape 3.29: 
Model Number: 3 of 8 with model Ensemble for Validation 3
3 - Ensemble with avg smape 3.27: 
Model Number: 4 of 8 with model Ensemble for Validation 3
📈 4 - Ensemble with avg smape 3.25: 
Model Number: 5 of 8 with model Ensemble for Validation 3
📈 5 - Ensemble with avg smape 2.46: 
Model Number: 6 of 8 with model Ensemble for Validation 3
6 - Ensemble with avg smape 3.15: 
Model Number: 7 of 8 with model Ensemble for Validation 3
7 - Ensemble with avg smape 2.5: 
Model Number: 8 of 8 with model Ensemble for Validation 3
8 - Ensemble with avg smape 2.46: 
                 Close
2023-02-20  438.245321
2023-02-21  436.408424
2023-02-22  435.946522
2023-02-23  435.098519
2023-02-24  434.784084
forecast

If you want to predict the stock price for the next 5 or 10 days you can use AutoTs Machine Learning Algorithm as shown above.

Step 4: Save Model

Goal:- In this step we are going to save our model in pickel format file.

import pickle
pickle.dump(log_R , open('TATA_Motors_Stock_Prediction_li.pkl', 'wb'))
pickle.dump(la , open('TATA_Motors_Stock_Prediction_la.pkl', 'wb'))
pickle.dump(ri , open('TATA_Motors_Stock_Prediction_ri.pkl', 'wb'))
import pickle

def model_prediction(features):
    
    pickled_model = pickle.load(open('TATA_Motors_Stock_Prediction_li.pkl', 'rb'))
    Close = str(pickled_model.predict(features)[0][0])
    
    return str(f'The TATA motors stock Price is closed at {Close}')

We can test our model by giving our own parameters or features to predict.

Open=498.000000
High=502.649994
Low=491.500000
Volume=15476803
model_prediction([[Open,High,Low,Volume]])
'The Stock Price is 495.5883288666215'

Conclusion

After observing the problem statement we have build an efficient model to overcome it. The above model helps predicting the TATA Motors stock price. It helps the investor in identifying the stock to be invest. The accuracy for the prediction is 98.65% and it signifies the accurate prediction of the stock.

Checkout whole project code here (github repo).

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