ForecasterSarimax

ForecasterSarimax(regressor, transformer_y=None, transformer_exog=None, fit_kwargs=None, forecaster_id=None)

This class turns ARIMA model from either the skforecast or pmdarima library into a Forecaster compatible with the skforecast API. New in version 0.10.0

Parameters:

Name	Type	Description	Default
regressor	(Sarimax, ARIMA)	An ARIMA model instance from either the skforecast or pmdarima library.	required
transformer_y	object transformer (preprocessor)	An instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API with methods: fit, transform, fit_transform and inverse_transform. ColumnTransformers are not allowed since they do not have inverse_transform method. The transformation is applied to y before training the forecaster.	`None`
transformer_exog	object transformer (preprocessor)	An instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API. The transformation is applied to exog before training the forecaster. inverse_transform is not available when using ColumnTransformers.	`None`
fit_kwargs	dict	Additional arguments to be passed to the fit method of the regressor. When using the skforecast Sarimax model, the fit kwargs should be passed using the model parameter sm_fit_kwargs and not this one.	`None`
forecaster_id	str, int default `None`	Name used as an identifier of the forecaster.	None

Attributes:

Name	Type	Description
regressor	(Sarimax, ARIMA)	An ARIMA model instance from either the skforecast or pmdarima library.
params	dict	Parameters of the sarimax model.
transformer_y	object transformer (preprocessor)	An instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API with methods: fit, transform, fit_transform and inverse_transform. ColumnTransformers are not allowed since they do not have inverse_transform method. The transformation is applied to y before training the forecaster.
transformer_exog	object transformer (preprocessor)	An instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API. The transformation is applied to exog before training the forecaster. inverse_transform is not available when using ColumnTransformers.
window_size	int	Not used, present here for API consistency by convention.
last_window	pandas Series	Last window the forecaster has seen during training. It stores the values needed to predict the next step immediately after the training data.
extended_index	pandas Index	When predicting using last_window and last_window_exog, the internal statsmodels SARIMAX will be updated using its append method. To do this, last_window data must start at the end of the index seen by the forecaster, this is stored in forecaster.extended_index. Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMAResults.append.html to know more about statsmodels append method.
fitted	bool	Tag to identify if the regressor has been fitted (trained).
index_type	type	Type of index of the input used in training.
index_freq	str	Frequency of Index of the input used in training.
training_range	pandas Index	First and last values of index of the data used during training.
included_exog	bool	If the forecaster has been trained using exogenous variable/s.
exog_type	type	Type of exogenous variable/s used in training.
exog_col_names	list	Names of the exogenous variables used during training.
fit_kwargs	dict	Additional arguments to be passed to the fit method of the regressor.
creation_date	str	Date of creation.
fit_date	str	Date of last fit.
skforecast_version	str	Version of skforecast library used to create the forecaster.
python_version	str	Version of python used to create the forecaster.
forecaster_id	(str, int)	Name used as an identifier of the forecaster.

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def __init__(
    self,
    regressor: ARIMA,
    transformer_y: Optional[object]=None,
    transformer_exog: Optional[object]=None,
    fit_kwargs: Optional[dict]=None,
    forecaster_id: Optional[Union[str, int]]=None
) -> None:

    self.regressor         = regressor
    self.transformer_y     = transformer_y
    self.transformer_exog  = transformer_exog
    self.window_size       = 1
    self.last_window       = None
    self.extended_index    = None
    self.fitted            = False
    self.index_type        = None
    self.index_freq        = None
    self.training_range    = None
    self.included_exog     = False
    self.exog_type         = None
    self.exog_col_names    = None
    self.creation_date     = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.fit_date          = None
    self.skforecast_version= skforecast.__version__
    self.python_version    = sys.version.split(" ")[0]
    self.forecaster_id     = forecaster_id

    if isinstance(self.regressor, pmdarima.arima.ARIMA):
        self.engine = 'pmdarima'
    elif isinstance(self.regressor, skforecast.Sarimax.Sarimax):
        self.engine = 'skforecast'
    else:
        raise TypeError(
            (f"`regressor` must be an instance of type pmdarima.arima.ARIMA "
             f"or skforecast.Sarimax.Sarimax. Got {type(regressor)}.")
        )

    self.params = self.regressor.get_params(deep=True)

    if self.engine == 'pmdarima':
        self.fit_kwargs = check_select_fit_kwargs(
                              regressor  = regressor,
                              fit_kwargs = fit_kwargs
                          )
    else:
        if fit_kwargs:
            warnings.warn(
                ("When using the skforecast Sarimax model, the fit kwargs should "
                 "be passed using the model parameter `sm_fit_kwargs`."),
                 IgnoredArgumentWarning
            )
        self.fit_kwargs = {}

fit(y, exog=None, store_last_window=True, suppress_warnings=False)

Training Forecaster.

Additional arguments to be passed to the fit method of the regressor can be added with the fit_kwargs argument when initializing the forecaster.

Parameters:

Name	Type	Description	Default
y	pandas Series	Training time series.	required
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s. Must have the same number of observations as y and their indexes must be aligned so that y[i] is regressed on exog[i].	`None`
store_last_window	bool	Whether or not to store the last window of training data.	`True`
suppress_warnings	bool	If True, warnings generated during fitting will be ignored.	`False`

Returns:

Type	Description
None

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def fit(
    self,
    y: pd.Series,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    store_last_window: bool=True,
    suppress_warnings: bool=False
) -> None:
    """
    Training Forecaster.

    Additional arguments to be passed to the `fit` method of the regressor 
    can be added with the `fit_kwargs` argument when initializing the forecaster.

    Parameters
    ----------
    y : pandas Series
        Training time series.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s. Must have the same
        number of observations as `y` and their indexes must be aligned so
        that y[i] is regressed on exog[i].
    store_last_window : bool, default `True`
        Whether or not to store the last window of training data.
    suppress_warnings : bool, default `False`
        If `True`, warnings generated during fitting will be ignored.

    Returns
    -------
    None

    """

    check_y(y=y)
    if exog is not None:
        if len(exog) != len(y):
            raise ValueError(
                (f"`exog` must have same number of samples as `y`. "
                 f"length `exog`: ({len(exog)}), length `y`: ({len(y)})")
            )
        check_exog(exog=exog)

    # Reset values in case the forecaster has already been fitted.
    self.index_type          = None
    self.index_freq          = None
    self.last_window         = None
    self.extended_index      = None
    self.included_exog       = False
    self.exog_type           = None
    self.exog_col_names      = None
    self.X_train_col_names   = None
    self.in_sample_residuals = None
    self.fitted              = False
    self.training_range      = None

    if exog is not None:
        self.included_exog = True
        self.exog_type = type(exog)
        self.exog_col_names = \
             exog.columns.to_list() if isinstance(exog, pd.DataFrame) else exog.name

    y = transform_series(
            series            = y,
            transformer       = self.transformer_y,
            fit               = True,
            inverse_transform = False
        )

    if exog is not None:
        if isinstance(exog, pd.Series):
            # pmdarima.arima.ARIMA only accepts DataFrames or 2d-arrays as exog   
            exog = exog.to_frame()

        exog = transform_dataframe(
                   df                = exog,
                   transformer       = self.transformer_exog,
                   fit               = True,
                   inverse_transform = False
               )

    if suppress_warnings:
        warnings.filterwarnings("ignore")

    if self.engine == 'pmdarima':
        self.regressor.fit(y=y, X=exog, **self.fit_kwargs)
    else:
        self.regressor.fit(y=y, exog=exog)

    if suppress_warnings:
        warnings.filterwarnings("default")

    self.fitted = True
    self.fit_date = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.training_range = y.index[[0, -1]]
    self.index_type = type(y.index)
    if isinstance(y.index, pd.DatetimeIndex):
        self.index_freq = y.index.freqstr
    else: 
        self.index_freq = y.index.step

    if store_last_window:
        self.last_window = y.copy()

    if self.engine == 'pmdarima':
        self.extended_index = self.regressor.arima_res_.fittedvalues.index.copy()
    else:
        self.extended_index = self.regressor.sarimax_res.fittedvalues.index.copy()

    self.params = self.regressor.get_params(deep=True)

predict(steps, last_window=None, last_window_exog=None, exog=None)

Forecast future values.

Generate predictions (forecasts) n steps in the future. Note that if exogenous variables were used in the model fit, they will be expected for the predict procedure and will fail otherwise.

When predicting using last_window and last_window_exog, the internal statsmodels SARIMAX will be updated using its append method. To do this, last_window data must start at the end of the index seen by the forecaster, this is stored in forecaster.extended_index.

Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMAResults.append.html to know more about statsmodels append method.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
last_window	pandas Series	Series values used to create the predictors needed in the predictions. Used to make predictions unrelated to the original data. Values have to start at the end of the training data.	`None`
last_window_exog	pandas Series, pandas DataFrame	Values of the exogenous variables aligned with last_window. Only needed when last_window is not None and the forecaster has been trained including exogenous variables. Used to make predictions unrelated to the original data. Values have to start at the end of the training data.	`None`
exog	pandas Series, pandas DataFrame	Value of the exogenous variable/s for the next steps.	`None`

Returns:

Name	Type	Description
predictions	pandas Series	Predicted values.

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def predict(
    self,
    steps: int,
    last_window: Optional[pd.Series]=None,
    last_window_exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> pd.Series:
    """
    Forecast future values.

    Generate predictions (forecasts) n steps in the future. Note that if 
    exogenous variables were used in the model fit, they will be expected 
    for the predict procedure and will fail otherwise.

    When predicting using `last_window` and `last_window_exog`, the internal
    statsmodels SARIMAX will be updated using its append method. To do this,
    `last_window` data must start at the end of the index seen by the 
    forecaster, this is stored in forecaster.extended_index.

    Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMAResults.append.html
    to know more about statsmodels append method.

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    last_window : pandas Series, default `None`
        Series values used to create the predictors needed in the 
        predictions. Used to make predictions unrelated to the original data. 
        Values have to start at the end of the training data.
    last_window_exog : pandas Series, pandas DataFrame, default `None`
        Values of the exogenous variables aligned with `last_window`. Only
        needed when `last_window` is not None and the forecaster has been
        trained including exogenous variables. Used to make predictions 
        unrelated to the original data. Values have to start at the end 
        of the training data.
    exog : pandas Series, pandas DataFrame, default `None`
        Value of the exogenous variable/s for the next steps.

    Returns
    -------
    predictions : pandas Series
        Predicted values.

    """

    # Needs to be a new variable to avoid arima_res_.append when using 
    # self.last_window. It already has it stored.
    last_window_check = last_window if last_window is not None else self.last_window

    check_predict_input(
        forecaster_name  = type(self).__name__,
        steps            = steps,
        fitted           = self.fitted,
        included_exog    = self.included_exog,
        index_type       = self.index_type,
        index_freq       = self.index_freq,
        window_size      = self.window_size,
        last_window      = last_window_check,
        last_window_exog = last_window_exog,
        exog             = exog,
        exog_type        = self.exog_type,
        exog_col_names   = self.exog_col_names,
        interval         = None,
        alpha            = None,
        max_steps        = None,
        levels           = None,
        series_col_names = None
    )

    # If not last_window is provided, last_window needs to be None
    if last_window is not None:
        last_window = last_window.copy()

    # When last_window_exog is provided but no last_window
    if last_window is None and last_window_exog is not None:
        raise ValueError(
            ("To make predictions unrelated to the original data, both "
             "`last_window` and `last_window_exog` must be provided.")
        )

    # Check if forecaster needs exog
    if last_window is not None and last_window_exog is None and self.included_exog:
        raise ValueError(
            ("Forecaster trained with exogenous variable/s. To make predictions "
             "unrelated to the original data, same variable/s must be provided "
             "using `last_window_exog`.")
        )

    if last_window is not None:
        # If predictions do not follow directly from the end of the training 
        # data. The internal statsmodels SARIMAX model needs to be updated 
        # using its append method. The data needs to start at the end of the 
        # training series.

        # check index append values
        expected_index = expand_index(index=self.extended_index, steps=1)[0]
        if expected_index != last_window.index[0]:
            raise ValueError(
                (f"To make predictions unrelated to the original data, `last_window` "
                 f"has to start at the end of the index seen by the forecaster.\n"
                 f"    Series last index         : {self.extended_index[-1]}.\n"
                 f"    Expected index            : {expected_index}.\n"
                 f"    `last_window` index start : {last_window.index[0]}.")
            )

        last_window = transform_series(
                          series            = last_window,
                          transformer       = self.transformer_y,
                          fit               = False,
                          inverse_transform = False
                      )

        # TODO -----------------------------------------------------------------------------------------------------
        # This is done because pmdarima deletes the series name
        # Check issue: https://github.com/alkaline-ml/pmdarima/issues/535
        if self.engine == 'pmdarima':
            last_window.name = None
        # ----------------------------------------------------------------------------------------------------------

        # last_window_exog
        if last_window_exog is not None:
            # check index last_window_exog
            if expected_index != last_window_exog.index[0]:
                raise ValueError(
                    (f"To make predictions unrelated to the original data, `last_window_exog` "
                     f"has to start at the end of the index seen by the forecaster.\n"
                     f"    Series last index              : {self.extended_index[-1]}.\n"
                     f"    Expected index                 : {expected_index}.\n"
                     f"    `last_window_exog` index start : {last_window_exog.index[0]}.")
                )

            if isinstance(last_window_exog, pd.Series):
                # pmdarima.arima.ARIMA only accepts DataFrames or 2d-arrays as exog 
                last_window_exog = last_window_exog.to_frame()

            last_window_exog = transform_dataframe(
                                   df                = last_window_exog,
                                   transformer       = self.transformer_exog,
                                   fit               = False,
                                   inverse_transform = False
                               )

        if self.engine == 'pmdarima':
            self.regressor.arima_res_ = self.regressor.arima_res_.append(
                                            endog = last_window,
                                            exog  = last_window_exog,
                                            refit = False
                                        )
            self.extended_index = self.regressor.arima_res_.fittedvalues.index
        else:
            self.regressor.append(
                y     = last_window,
                exog  = last_window_exog,
                refit = False
            )
            self.extended_index = self.regressor.sarimax_res.fittedvalues.index

    # Exog
    if exog is not None:
        if isinstance(exog, pd.Series):
            # pmdarima.arima.ARIMA only accepts DataFrames or 2d-arrays as exog
            exog = exog.to_frame()

        exog = transform_dataframe(
                   df                = exog,
                   transformer       = self.transformer_exog,
                   fit               = False,
                   inverse_transform = False
               )  
        exog = exog.iloc[:steps, ]

    # Get following n steps predictions
    if self.engine == 'pmdarima':
        predictions = self.regressor.predict(
                          n_periods = steps,
                          X         = exog
                      )
    else:
        predictions = self.regressor.predict(
                          steps = steps,
                          exog  = exog
                      )
        predictions = predictions.iloc[:, 0]

    # Reverse the transformation if needed
    predictions = transform_series(
                      series            = predictions,
                      transformer       = self.transformer_y,
                      fit               = False,
                      inverse_transform = True
                  )
    predictions.name = 'pred'

    return predictions

predict_interval(steps, last_window=None, last_window_exog=None, exog=None, alpha=0.05, interval=None)

Forecast future values and their confidence intervals.

Generate predictions (forecasts) n steps in the future with confidence intervals. Note that if exogenous variables were used in the model fit, they will be expected for the predict procedure and will fail otherwise.

When predicting using last_window and last_window_exog, the internal statsmodels SARIMAX will be updated using its append method. To do this, last_window data must start at the end of the index seen by the forecaster, this is stored in forecaster.extended_index.

Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMAResults.append.html to know more about statsmodels append method.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
last_window	pandas Series	Series values used to create the predictors needed in the predictions. Used to make predictions unrelated to the original data. Values have to start at the end of the training data.	`None`
last_window_exog	pandas Series, pandas DataFrame	Values of the exogenous variables aligned with last_window. Only need when last_window is not None and the forecaster has been trained including exogenous variables.	`None`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s.	`None`
alpha	float	The confidence intervals for the forecasts are (1 - alpha) %. If both, alpha and interval are provided, alpha will be used.	`0.05`
interval	list	Confidence of the prediction interval estimated. The values must be symmetric. Sequence of percentiles to compute, which must be between 0 and 100 inclusive. For example, interval of 95% should be as interval = [2.5, 97.5]. If both, alpha and interval are provided, alpha will be used.	`None`

Returns:

Name	Type	Description
predictions	pandas DataFrame	Values predicted by the forecaster and their estimated interval. pred: predictions. lower_bound: lower bound of the interval. upper_bound: upper bound of the interval.

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def predict_interval(
    self,
    steps: int,
    last_window: Optional[pd.Series]=None,
    last_window_exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    alpha: float=0.05,
    interval: list=None,
) -> pd.DataFrame:
    """
    Forecast future values and their confidence intervals.

    Generate predictions (forecasts) n steps in the future with confidence
    intervals. Note that if exogenous variables were used in the model fit, 
    they will be expected for the predict procedure and will fail otherwise.

    When predicting using `last_window` and `last_window_exog`, the internal
    statsmodels SARIMAX will be updated using its append method. To do this,
    `last_window` data must start at the end of the index seen by the 
    forecaster, this is stored in forecaster.extended_index.

    Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.arima.model.ARIMAResults.append.html
    to know more about statsmodels append method.

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    last_window : pandas Series, default `None`
        Series values used to create the predictors needed in the 
        predictions. Used to make predictions unrelated to the original data. 
        Values have to start at the end of the training data.
    last_window_exog : pandas Series, pandas DataFrame, default `None`
        Values of the exogenous variables aligned with `last_window`. Only
        need when `last_window` is not None and the forecaster has been
        trained including exogenous variables.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s.
    alpha : float, default `0.05`
        The confidence intervals for the forecasts are (1 - alpha) %.
        If both, `alpha` and `interval` are provided, `alpha` will be used.
    interval : list, default `None`
        Confidence of the prediction interval estimated. The values must be
        symmetric. Sequence of percentiles to compute, which must be between 
        0 and 100 inclusive. For example, interval of 95% should be as 
        `interval = [2.5, 97.5]`. If both, `alpha` and `interval` are 
        provided, `alpha` will be used.

    Returns
    -------
    predictions : pandas DataFrame
        Values predicted by the forecaster and their estimated interval.

        - pred: predictions.
        - lower_bound: lower bound of the interval.
        - upper_bound: upper bound of the interval.

    """

    # Needs to be a new variable to avoid arima_res_.append when using 
    # self.last_window. It already has it stored.
    last_window_check = last_window if last_window is not None else self.last_window

    check_predict_input(
        forecaster_name  = type(self).__name__,
        steps            = steps,
        fitted           = self.fitted,
        included_exog    = self.included_exog,
        index_type       = self.index_type,
        index_freq       = self.index_freq,
        window_size      = self.window_size,
        last_window      = last_window_check,
        last_window_exog = last_window_exog,
        exog             = exog,
        exog_type        = self.exog_type,
        exog_col_names   = self.exog_col_names,
        interval         = interval,
        alpha            = alpha,
        max_steps        = None,
        levels           = None,
        series_col_names = None
    )

    # If not last_window is provided, last_window needs to be None
    if last_window is not None:
        last_window = last_window.copy()

    # If last_window_exog is provided but no last_window
    if last_window is None and last_window_exog is not None:
        raise ValueError(
            ("To make predictions unrelated to the original data, both "
             "`last_window` and `last_window_exog` must be provided.")
        )

    # Check if forecaster needs exog
    if last_window is not None and last_window_exog is None and self.included_exog:
        raise ValueError(
            ("Forecaster trained with exogenous variable/s. To make predictions "
             "unrelated to the original data, same variable/s must be provided "
             "using `last_window_exog`.")
        )  

    # If interval and alpha take alpha, if interval transform to alpha
    if alpha is None:
        if 100 - interval[1] != interval[0]:
            raise ValueError(
                (f"When using `interval` in ForecasterSarimax, it must be symmetrical. "
                 f"For example, interval of 95% should be as `interval = [2.5, 97.5]`. "
                 f"Got {interval}.")
            )
        alpha = 2*(100 - interval[1])/100

    if last_window is not None:
        # If predictions do not follow directly from the end of the training 
        # data. The internal statsmodels SARIMAX model needs to be updated 
        # using its append method. The data needs to start at the end of the 
        # training series.

        # check index append values
        expected_index = expand_index(index=self.extended_index, steps=1)[0]
        if expected_index != last_window.index[0]:
            raise ValueError(
                (f"To make predictions unrelated to the original data, `last_window` "
                 f"has to start at the end of the index seen by the forecaster.\n"
                 f"    Series last index         : {self.extended_index[-1]}.\n"
                 f"    Expected index            : {expected_index}.\n"
                 f"    `last_window` index start : {last_window.index[0]}.")
            )

        last_window = transform_series(
                          series            = last_window,
                          transformer       = self.transformer_y,
                          fit               = False,
                          inverse_transform = False
                      )

        # TODO -----------------------------------------------------------------------------------------------------
        # This is done because pmdarima deletes the series name
        # Check issue: https://github.com/alkaline-ml/pmdarima/issues/535
        if self.engine == 'pmdarima':
            last_window.name = None
        # ----------------------------------------------------------------------------------------------------------

        # Transform last_window_exog    
        if last_window_exog is not None:
            # check index last_window_exog
            if expected_index != last_window_exog.index[0]:
                raise ValueError(
                    (f"To make predictions unrelated to the original data, `last_window_exog` "
                     f"has to start at the end of the index seen by the forecaster.\n"
                     f"    Series last index              : {self.extended_index[-1]}.\n"
                     f"    Expected index                 : {expected_index}.\n"
                     f"    `last_window_exog` index start : {last_window_exog.index[0]}.")
                )

            if isinstance(last_window_exog, pd.Series):
                # pmdarima.arima.ARIMA only accepts DataFrames or 2d-arrays as exog 
                last_window_exog = last_window_exog.to_frame()

            last_window_exog = transform_dataframe(
                                   df                = last_window_exog,
                                   transformer       = self.transformer_exog,
                                   fit               = False,
                                   inverse_transform = False
                               )

        if self.engine == 'pmdarima':
            self.regressor.arima_res_ = self.regressor.arima_res_.append(
                                            endog = last_window,
                                            exog  = last_window_exog,
                                            refit = False
                                        )
            self.extended_index = self.regressor.arima_res_.fittedvalues.index
        else:
            self.regressor.append(
                y     = last_window,
                exog  = last_window_exog,
                refit = False
            )
            self.extended_index = self.regressor.sarimax_res.fittedvalues.index

    # Exog
    if exog is not None:
        if isinstance(exog, pd.Series):
            # pmdarima.arima.ARIMA only accepts DataFrames or 2d-arrays as exog
            exog = exog.to_frame()

        exog = transform_dataframe(
                   df                = exog,
                   transformer       = self.transformer_exog,
                   fit               = False,
                   inverse_transform = False
               )  
        exog = exog.iloc[:steps, ]

    # Get following n steps predictions with intervals
    if self.engine == 'pmdarima':
        predicted_mean, conf_int = self.regressor.predict(
                                       n_periods       = steps,
                                       X               = exog,
                                       return_conf_int = True,
                                       alpha           = alpha
                                   )

        predictions = predicted_mean.to_frame(name="pred")
        predictions['lower_bound'] = conf_int[:, 0]
        predictions['upper_bound'] = conf_int[:, 1]
    else:
        predictions = self.regressor.predict(
                          steps = steps,
                          exog  = exog,
                          return_conf_int = True,
                          alpha = alpha
                      )


    # Reverse the transformation if needed
    if self.transformer_y:
        for col in predictions.columns:
            predictions[col] = transform_series(
                                series            = predictions[col],
                                transformer       = self.transformer_y,
                                fit               = False,
                                inverse_transform = True
                           )

    return predictions

set_params(params)

Set new values to the parameters of the model stored in the forecaster.

Parameters:

Name	Type	Description	Default
params	dict	Parameters values.	required

Returns:

Type	Description
None

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def set_params(
    self, 
    params: dict
) -> None:
    """
    Set new values to the parameters of the model stored in the forecaster.

    Parameters
    ----------
    params : dict
        Parameters values.

    Returns
    -------
    None

    """

    self.regressor = clone(self.regressor)
    self.regressor.set_params(**params)
    self.params = self.regressor.get_params(deep=True)

set_fit_kwargs(fit_kwargs)

Set new values for the additional keyword arguments passed to the fit method of the regressor.

Parameters:

Name	Type	Description	Default
fit_kwargs	dict	Dict of the form {"argument": new_value}.	required

Returns:

Type	Description
None

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def set_fit_kwargs(
    self, 
    fit_kwargs: dict
) -> None:
    """
    Set new values for the additional keyword arguments passed to the `fit` 
    method of the regressor.

    Parameters
    ----------
    fit_kwargs : dict
        Dict of the form {"argument": new_value}.

    Returns
    -------
    None

    """

    if self.engine == 'pmdarima':
        self.fit_kwargs = check_select_fit_kwargs(self.regressor, fit_kwargs=fit_kwargs)
    else:
        warnings.warn(
            ("When using the skforecast Sarimax model, the fit kwargs should "
             "be passed using the model parameter `sm_fit_kwargs`."),
             IgnoredArgumentWarning
        )
        self.fit_kwargs = {}

get_feature_importances(sort_importance=True)

Return feature importances of the regressor stored in the forecaster.

Parameters:

Name	Type	Description	Default
sort_importance	bool	If True, sorts the feature importances in descending order.	True

Returns:

Name	Type	Description
feature_importances	pandas DataFrame	Feature importances associated with each predictor.

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def get_feature_importances(
    self,
    sort_importance: bool=True
) -> pd.DataFrame:
    """
    Return feature importances of the regressor stored in the forecaster.

    Parameters
    ----------
    sort_importance: bool, default `True`
        If `True`, sorts the feature importances in descending order.

    Returns
    -------
    feature_importances : pandas DataFrame
        Feature importances associated with each predictor.

    """

    if not self.fitted:
        raise NotFittedError(
            ("This forecaster is not fitted yet. Call `fit` with appropriate "
             "arguments before using `get_feature_importances()`.")
        )

    feature_importances = self.regressor.params().to_frame().reset_index()
    feature_importances.columns = ['feature', 'importance']

    if sort_importance:
        feature_importances = feature_importances.sort_values(
                                  by='importance', ascending=False
                              )

    return feature_importances

get_info_criteria(criteria='aic', method='standard')

Get the selected information criteria.

Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAXResults.info_criteria.html to know more about statsmodels info_criteria method.

Parameters:

Name	Type	Description	Default
criteria	str	The information criteria to compute. Valid options are {'aic', 'bic', 'hqic'}.	`'aic'`
method	str	The method for information criteria computation. Default is 'standard' method; 'lutkepohl' computes the information criteria as in Lütkepohl (2007).	`'standard'`

Returns:

Name	Type	Description
metric	float	The value of the selected information criteria.

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def get_info_criteria(
    self, 
    criteria: str='aic', 
    method: str='standard'
) -> float:
    """
    Get the selected information criteria.

    Check https://www.statsmodels.org/dev/generated/statsmodels.tsa.statespace.sarimax.SARIMAXResults.info_criteria.html
    to know more about statsmodels info_criteria method.

    Parameters
    ----------
    criteria : str, default `'aic'`
        The information criteria to compute. Valid options are {'aic', 'bic',
        'hqic'}.
    method : str, default `'standard'`
        The method for information criteria computation. Default is 'standard'
        method; 'lutkepohl' computes the information criteria as in Lütkepohl
        (2007).

    Returns
    -------
    metric : float
        The value of the selected information criteria.

    """

    if criteria not in ['aic', 'bic', 'hqic']:
        raise ValueError(
            (f"Invalid value for `criteria`. Valid options are 'aic', 'bic', "
             f"and 'hqic'.")
        )

    if method not in ['standard', 'lutkepohl']:
        raise ValueError(
            (f"Invalid value for `method`. Valid options are 'standard' and "
             f"'lutkepohl'.")
        )

    if self.engine == 'pmdarima':
        metric = self.regressor.arima_res_.info_criteria(criteria=criteria, method=method)
    else:
        metric = self.regressor.get_info_criteria(criteria=criteria, method=method)

    return metric

summary()

Show forecaster information.

Parameters:

Name	Type	Description	Default
self			required

Returns:

Type	Description
None

Source code in skforecast\ForecasterSarimax\ForecasterSarimax.py

def summary(self) -> None:
    """
    Show forecaster information.

    Parameters
    ----------
    self

    Returns
    -------
    None

    """

    print(self)