Understanding the forecaster attributes¶

During the process of creating and training a forecaster, the object stores a lot of information in its attributes that can be useful to the user. We will explore the main attributes included in a ForecasterAutoreg, but this can be extrapolated to any of the skforecast forecasters.

Create and train a forecaster¶

To be able to create and train a forecaster, at least regressor and lags must be specified.

In [1]:

# Libraries
# ==============================================================================
import pandas as pd
from skforecast.ForecasterAutoreg import ForecasterAutoreg
from sklearn.ensemble import RandomForestRegressor

# Libraries # ============================================================================== import pandas as pd from skforecast.ForecasterAutoreg import ForecasterAutoreg from sklearn.ensemble import RandomForestRegressor

In [2]:

# Download data
# ==============================================================================
url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv')
data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime'])

# Data preprocessing
# ==============================================================================
data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d')
data = data.set_index('datetime')
data = data.asfreq('MS')
data = data.sort_index()

# Create and fit forecaster
# ==============================================================================
forecaster = ForecasterAutoreg(
                 regressor = RandomForestRegressor(random_state=123),
                 lags      = 5
             )

forecaster.fit(y=data['y'])
forecaster

# Download data # ============================================================================== url = ('https://raw.githubusercontent.com/JoaquinAmatRodrigo/skforecast/master/data/h2o.csv') data = pd.read_csv(url, sep=',', header=0, names=['y', 'datetime']) # Data preprocessing # ============================================================================== data['datetime'] = pd.to_datetime(data['datetime'], format='%Y/%m/%d') data = data.set_index('datetime') data = data.asfreq('MS') data = data.sort_index() # Create and fit forecaster # ============================================================================== forecaster = ForecasterAutoreg( regressor = RandomForestRegressor(random_state=123), lags = 5 ) forecaster.fit(y=data['y']) forecaster

Out[2]:

================= 
ForecasterAutoreg 
================= 
Regressor: RandomForestRegressor(random_state=123) 
Lags: [1 2 3 4 5] 
Transformer for y: None 
Transformer for exog: None 
Window size: 5 
Weight function included: False 
Exogenous included: False 
Type of exogenous variable: None 
Exogenous variables names: None 
Training range: [Timestamp('1991-07-01 00:00:00'), Timestamp('2008-06-01 00:00:00')] 
Training index type: DatetimeIndex 
Training index frequency: MS 
Regressor parameters: {'bootstrap': True, 'ccp_alpha': 0.0, 'criterion': 'squared_error', 'max_depth': None, 'max_features': 1.0, 'max_leaf_nodes': None, 'max_samples': None, 'min_impurity_decrease': 0.0, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100, 'n_jobs': None, 'oob_score': False, 'random_state': 123, 'verbose': 0, 'warm_start': False} 
fit_kwargs: {} 
Creation date: 2023-05-19 20:48:12 
Last fit date: 2023-05-19 20:48:12 
Skforecast version: 0.8.0 
Python version: 3.9.13 
Forecaster id: None 

In [3]:

# List of attributes
# ==============================================================================
for attribute, value in forecaster.__dict__.items():
    print(attribute)

# List of attributes # ============================================================================== for attribute, value in forecaster.__dict__.items(): print(attribute)

regressor
transformer_y
transformer_exog
weight_func
source_code_weight_func
last_window
index_type
index_freq
training_range
included_exog
exog_type
exog_dtypes
exog_col_names
X_train_col_names
in_sample_residuals
out_sample_residuals
fitted
creation_date
fit_date
skforcast_version
python_version
forecaster_id
lags
max_lag
window_size
fit_kwargs

Regressor¶

Skforecast is a Python library that facilitates using scikit-learn regressors as multi-step forecasters and also works with any regressor compatible with the scikit-learn API.

In [4]:

# Forecaster regressor
# ==============================================================================
forecaster.regressor

# Forecaster regressor # ============================================================================== forecaster.regressor

Out[4]:

RandomForestRegressor(random_state=123)

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  Note

In the forecasters that follows a Direct Strategy, one instance of the regressor is trained for each step. All of them are stored in self.regressors_

Lags¶

Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1.

In [5]:

# Forecaster lags
# ==============================================================================
forecaster.lags

# Forecaster lags # ============================================================================== forecaster.lags

Out[5]:

array([1, 2, 3, 4, 5])

Last window¶

Last window the forecaster has seen during training. It stores the values needed to predict the next step immediately after the training data.

In [6]:

# Forecaster last window
# ==============================================================================
forecaster.last_window

# Forecaster last window # ============================================================================== forecaster.last_window

Out[6]:

datetime
2008-02-01    0.761822
2008-03-01    0.649435
2008-04-01    0.827887
2008-05-01    0.816255
2008-06-01    0.762137
Freq: MS, Name: y, dtype: float64

  Note

learn how to get your forecasters into production and get the most out of them with last_window. Using forecaster models in production.

Window size¶

The size of the data window needed to create the predictors. It is equal to forecaster.max_lag.

In [7]:

# Forecaster window size
# ==============================================================================
forecaster.window_size

# Forecaster window size # ============================================================================== forecaster.window_size

Out[7]:

5

In sample residuals¶

Residuals from models predicting training data. Only stored up to 1000 values. If transformer_series is not None, the residuals are stored in the transformed scale.

  Note

In the forecasters that follows a Direct Strategy and in the Independent multi-series forecasting this parameter is a dict containing the residuals for each regressor/serie.

In [8]:

# Forecaster in sample residuals
# ==============================================================================
print("Length:", len(forecaster.in_sample_residuals))
forecaster.in_sample_residuals[:5]

# Forecaster in sample residuals # ============================================================================== print("Length:", len(forecaster.in_sample_residuals)) forecaster.in_sample_residuals[:5]

Length: 199

Out[8]:

array([ 0.02232928,  0.0597808 , -0.10463712, -0.03318622, -0.00291908])

Out sample residuals¶

Residuals from models predicting non training data. Only stored up to 1000 values. If transformer_y is not None, residuals are assumed to be in the transformed scale. Use set_out_sample_residuals method to set values.

As no values have been added, the parameter is None.

In [9]:

# Forecaster out sample residuals
# ==============================================================================
forecaster.out_sample_residuals

# Forecaster out sample residuals # ============================================================================== forecaster.out_sample_residuals

In [10]:

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