ForecasterAutoregMultiSeriesCustom

ForecasterAutoregMultiSeriesCustom(regressor, fun_predictors, window_size, name_predictors=None, transformer_series=None, transformer_exog=None, weight_func=None, series_weights=None, fit_kwargs=None, forecaster_id=None)

Bases: ForecasterBase

This class turns any regressor compatible with the scikit-learn API into a recursive autoregressive (multi-step) forecaster for multiple series with a custom function to create predictors. New in version 0.7.0

Parameters:

Name	Type	Description	Default
regressor	regressor or pipeline compatible with the scikit-learn API	An instance of a regressor or pipeline compatible with the scikit-learn API.	required
fun_predictors	Callable	Function that receives a time series as input (numpy ndarray) and returns another numpy ndarray with the predictors. The same function is applied to all series.	required
window_size	int	Size of the window needed by fun_predictors to create the predictors.	required
name_predictors	list	Name of the predictors returned by fun_predictors. If None, predictors are named using the prefix 'custom_predictor_' where i is the index of the position the predictor has in the returned array of fun_predictors.	`None`
transformer_series	transformer(preprocessor), dict	An instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API with methods: fit, transform, fit_transform and inverse_transform. Transformation is applied to each series before training the forecaster. ColumnTransformers are not allowed since they do not have inverse_transform method. If single transformer: it is cloned and applied to all series. If dict of transformers: a different transformer can be used for each series.	`None`
transformer_exog	transformer	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`
weight_func	Callable, dict	Function that defines the individual weights for each sample based on the index. For example, a function that assigns a lower weight to certain dates. Ignored if regressor does not have the argument sample_weight in its fit method. See Notes section for more details on the use of the weights. If single function: it is applied to all series. If dict {'series_column_name' : Callable}: a different function can be used for each series, a weight of 1 is given to all series not present in weight_func.	`None`
series_weights	dict	Weights associated with each series {'series_column_name' : float}. It is only applied if the regressor used accepts sample_weight in its fit method. See Notes section for more details on the use of the weights. If a dict is provided, a weight of 1 is given to all series not present in series_weights. If None, all levels have the same weight.	`None`
fit_kwargs	dict	Additional arguments to be passed to the fit method of the regressor. New in version 0.8.0	`None`
forecaster_id	str, int	Name used as an identifier of the forecaster. New in version 0.7.0	`None`

Attributes:

Name	Type	Description
regressor	regressor or pipeline compatible with the scikit-learn API	An instance of a regressor or pipeline compatible with the scikit-learn API.
fun_predictors	Callable	Function that receives a time series as input (numpy ndarray) and returns another numpy ndarray with the predictors. The same function is applied to all series.
source_code_fun_predictors	str	Source code of the custom function used to create the predictors.
window_size	int	Size of the window needed by fun_predictors to create the predictors.
name_predictors	list	Name of the predictors returned by fun_predictors. If None, predictors are named using the prefix 'custom_predictor_' where i is the index of the position the predictor has in the returned array of fun_predictors.
transformer_series	transformer(preprocessor), dict	An instance of a transformer (preprocessor) compatible with the scikit-learn preprocessing API with methods: fit, transform, fit_transform and inverse_transform. Transformation is applied to each series before training the forecaster. ColumnTransformers are not allowed since they do not have inverse_transform method. If single transformer: it is cloned and applied to all series. If dict of transformers: a different transformer can be used for each series.
transformer_series_	dict	Dictionary with the transformer for each series. It is created cloning the objects in transformer_series and is used internally to avoid overwriting.
transformer_exog	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.
weight_func	Callable, dict	Function that defines the individual weights for each sample based on the index. For example, a function that assigns a lower weight to certain dates. Ignored if regressor does not have the argument sample_weight in its fit method. See Notes section for more details on the use of the weights. If single function: it is applied to all series. If dict {'series_column_name' : Callable}: a different function can be used for each series, a weight of 1 is given to all series not present in weight_func.
weight_func_	dict	Dictionary with the weight_func for each series. It is created cloning the objects in weight_func and is used internally to avoid overwriting.
source_code_weight_func	str, dict	Source code of the custom function(s) used to create weights.
series_weights	dict	Weights associated with each series {'series_column_name' : float}. It is only applied if the regressor used accepts sample_weight in its fit method. See Notes section for more details on the use of the weights. If a dict is provided, a weight of 1 is given to all series not present in series_weights. If None, all levels have the same weight.
series_weights_	dict	Weights associated with each series.It is created as a clone of series_weights and is used internally to avoid overwriting.
window_size	int	Size of the window needed by fun_predictors to create the predictors.
last_window	pandas Series	Last window seen by the forecaster during training. It stores the values needed to predict the next step immediately after the training data.
index_type	type	Type of index of the input used in training.
index_freq	str	Frequency of Index of the input used in training.
index_values	pandas Index	Values 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_dtypes	dict	Type of each exogenous variable/s used in training. If transformer_exog is used, the dtypes are calculated after the transformation.
exog_col_names	list	Names of columns of exog if exog used in training was a pandas DataFrame.
series_col_names	list	Names of the series (levels) used during training.
X_train_col_names	list	Names of columns of the matrix created internally for training.
fit_kwargs	dict	Additional arguments to be passed to the fit method of the regressor. New in version 0.8.0
in_sample_residuals	dict	Residuals of the model when predicting training data. Only stored up to 1000 values in the form {level: residuals}. If transformer_series is not None, residuals are stored in the transformed scale.
out_sample_residuals	dict	Residuals of the model when predicting non-training data. Only stored up to 1000 values in the form {level: residuals}. If transformer_series is not None, residuals are assumed to be in the transformed scale. Use set_out_sample_residuals() method to set values.
fitted	bool	Tag to identify if the regressor has been fitted (trained).
creation_date	str	Date of creation.
fit_date	str	Date of last fit.
skforcast_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.

Notes

The weights are used to control the influence that each observation has on the training of the model. ForecasterAutoregMultiseries accepts two types of weights. If the two types of weights are indicated, they are multiplied to create the final weights. The resulting sample_weight cannot have negative values.

• series_weights : controls the relative importance of each series. If a series has twice as much weight as the others, the observations of that series influence the training twice as much. The higher the weight of a series relative to the others, the more the model will focus on trying to learn that series.
• weight_func : controls the relative importance of each observation according to its index value. For example, a function that assigns a lower weight to certain dates.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def __init__(
    self,
    regressor: object,
    fun_predictors: Callable, 
    window_size: int,
    name_predictors: Optional[list]=None,
    transformer_series: Optional[Union[object, dict]]=None,
    transformer_exog: Optional[object]=None,
    weight_func: Optional[Union[Callable, dict]]=None,
    series_weights: Optional[dict]=None,
    fit_kwargs: Optional[dict]=None,
    forecaster_id: Optional[Union[str, int]]=None
) -> None:

    self.regressor                  = regressor
    self.fun_predictors             = fun_predictors
    self.source_code_fun_predictors = None
    self.window_size                = window_size
    self.name_predictors            = name_predictors
    self.transformer_series         = transformer_series
    self.transformer_series_        = None
    self.transformer_exog           = transformer_exog
    self.weight_func                = weight_func
    self.weight_func_               = None
    self.source_code_weight_func    = None
    self.series_weights             = series_weights
    self.series_weights_            = None
    self.index_type                 = None
    self.index_freq                 = None
    self.index_values               = None
    self.training_range             = None
    self.last_window                = None
    self.included_exog              = False
    self.exog_type                  = None
    self.exog_dtypes                = None
    self.exog_col_names             = None
    self.series_col_names           = None
    self.X_train_col_names          = None
    self.in_sample_residuals        = None
    self.out_sample_residuals       = None
    self.fitted                     = False
    self.creation_date              = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.fit_date                   = None
    self.skforcast_version          = skforecast.__version__
    self.python_version             = sys.version.split(" ")[0]
    self.forecaster_id              = forecaster_id

    if not isinstance(window_size, int):
        raise TypeError(
            f"Argument `window_size` must be an int. Got {type(window_size)}."
        )

    if not isinstance(fun_predictors, Callable):
        raise TypeError(
            f"Argument `fun_predictors` must be a Callable. Got {type(fun_predictors)}."
        )

    self.source_code_fun_predictors = inspect.getsource(fun_predictors)

    self.weight_func, self.source_code_weight_func, self.series_weights = initialize_weights(
        forecaster_name = type(self).__name__, 
        regressor       = regressor, 
        weight_func     = weight_func, 
        series_weights  = series_weights
    )

    self.fit_kwargs = check_select_fit_kwargs(
                          regressor  = regressor,
                          fit_kwargs = fit_kwargs
                      )

create_train_X_y(series, exog=None)

Create training matrices from multiple time series and exogenous variables.

Parameters:

Name	Type	Description	Default
series	pandas DataFrame	Training time series.	required
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s. Must have the same number of observations as series and their indexes must be aligned.	`None`

Returns:

Name	Type	Description
X_train	pandas DataFrame	Training values (predictors).
y_train	pandas Series	Values (target) of the time series related to each row of X_train. Shape: (len(series) - self.max_lag, )
y_index	pandas Index	Index of series.
y_train_index	pandas Index	Index of y_train.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def create_train_X_y(
    self,
    series: pd.DataFrame,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> Tuple[pd.DataFrame, pd.Series, pd.Index, pd.Index]:
    """
    Create training matrices from multiple time series and exogenous
    variables.

    Parameters
    ----------
    series : pandas DataFrame
        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 `series` and their indexes must be aligned.

    Returns
    -------
    X_train : pandas DataFrame
        Training values (predictors).
    y_train : pandas Series
        Values (target) of the time series related to each row of `X_train`.
        Shape: (len(series) - self.max_lag, )
    y_index : pandas Index
        Index of `series`.
    y_train_index: pandas Index
        Index of `y_train`.

    """

    if not isinstance(series, pd.DataFrame):
        raise TypeError(f"`series` must be a pandas DataFrame. Got {type(series)}.")

    if len(series) < self.window_size + 1:
        raise ValueError(
            (f"`series` must have as many values as the windows_size needed by "
             f"{self.fun_predictors.__name__}. For this Forecaster the "
             f"minimum length is {self.window_size + 1}")
        )

    series_col_names = list(series.columns)

    if self.transformer_series is None:
        self.transformer_series_ = {serie: None for serie in series_col_names}
    elif not isinstance(self.transformer_series, dict):
        self.transformer_series_ = {serie: clone(self.transformer_series) 
                                    for serie in series_col_names}
    else:
        self.transformer_series_ = {serie: None for serie in series_col_names}
        # Only elements already present in transformer_series_ are updated
        self.transformer_series_.update(
            (k, v) for k, v in deepcopy(self.transformer_series).items() 
            if k in self.transformer_series_
        )
        series_not_in_transformer_series = set(series.columns) - set(self.transformer_series.keys())
        if series_not_in_transformer_series:
                warnings.warn(
                    (f"{series_not_in_transformer_series} not present in `transformer_series`."
                     f" No transformation is applied to these series."),
                     IgnoredArgumentWarning
                )

    if exog is not None:
        if len(exog) != len(series):
            raise ValueError(
                (f"`exog` must have same number of samples as `series`. "
                 f"length `exog`: ({len(exog)}), length `series`: ({len(series)})")
            )
        check_exog(exog=exog, allow_nan=True)
        if isinstance(exog, pd.Series):
            exog = transform_series(
                       series            = exog,
                       transformer       = self.transformer_exog,
                       fit               = True,
                       inverse_transform = False
                   )
        else:
            exog = transform_dataframe(
                       df                = exog,
                       transformer       = self.transformer_exog,
                       fit               = True,
                       inverse_transform = False
                   )

        check_exog(exog=exog, allow_nan=False)
        check_exog_dtypes(exog)
        self.exog_dtypes = get_exog_dtypes(exog=exog)

        _, exog_index = preprocess_exog(exog=exog, return_values=False)
        if not (exog_index[:len(series.index)] == series.index).all():
            raise ValueError(
                ("Different index for `series` and `exog`. They must be equal "
                 "to ensure the correct alignment of values.")
            )

    X_levels = []
    len_series = []

    for i, serie in enumerate(series.columns):

        y = series[serie]
        y_values = y.to_numpy()

        if np.isnan(y_values).all():
            raise ValueError(f"All values of series '{serie}' are NaN.")

        first_no_nan_idx = np.argmax(~np.isnan(y_values))
        y_values = y_values[first_no_nan_idx:]

        if np.isnan(y_values).any():
            raise ValueError(
                (f"'{serie}' Time series has missing values in between or "
                 f"at the end of the time series. When working with series "
                 f"of different lengths, all series must be complete after "
                 f"the first non-null value.")
            )

        y = transform_series(
                series            = y.iloc[first_no_nan_idx:],
                transformer       = self.transformer_series_[serie],
                fit               = True,
                inverse_transform = False
            )

        y_values = y.to_numpy()

        X_train_values  = []
        y_train_values  = []

        for j in range(len(y) - self.window_size):

            temp_X_index = np.arange(j, self.window_size + j)
            temp_y_index  = self.window_size + j

            X_train_values.append(self.fun_predictors(y=y_values[temp_X_index]))
            y_train_values.append(y_values[temp_y_index])

        X_train_values = np.vstack(X_train_values)
        y_train_values = np.array(y_train_values)

        if np.isnan(X_train_values).any():
            raise ValueError(
                f"`fun_predictors()` is returning `NaN` values for series '{serie}'."
            )

        if i == 0:
            X_train = X_train_values
            y_train = y_train_values
        else:
            X_train = np.concatenate((X_train, X_train_values), axis=0)
            y_train = np.concatenate((y_train, y_train_values), axis=0)

        X_level = [serie]*len(X_train_values)
        X_levels.extend(X_level)
        len_series.append(len(y_train_values))

    if self.name_predictors is None:
        X_train_col_names = [f"custom_predictor_{i}" 
                             for i in range(X_train.shape[1])]
    else:
        if len(self.name_predictors) != X_train.shape[1]:
            raise ValueError(
                ("The length of provided predictors names (`name_predictors`) do "
                 "not match the number of columns created by `fun_predictors()`.")
            )
        X_train_col_names = self.name_predictors.copy()

    # y_values correspond only to the last series of `series`. Since the columns
    # of X_train are the same for all series, the check is the same.
    expected = self.fun_predictors(y_values[:-1])
    observed = X_train[-1, :]

    if expected.shape != observed.shape or not (expected == observed).all():
        raise ValueError(
            (f"The `window_size` argument ({self.window_size}), declared when "
             f"initializing the forecaster, does not correspond to the window "
             f"used by `fun_predictors()`.")
        )

    X_levels = pd.Series(X_levels)
    X_levels = pd.get_dummies(X_levels, dtype=float)

    X_train = pd.DataFrame(
                  data    = X_train,
                  columns = X_train_col_names
              )

    if exog is not None:
        # The first `self.window_size` positions have to be removed from exog
        # since they are not in X_train. Then Exog is cloned as many times 
        # as there are series, taking into account the length of the series.
        exog_to_train = [exog.iloc[-length:, ] for length in len_series]
        exog_to_train = pd.concat(exog_to_train).reset_index(drop=True)
    else:
        exog_to_train = None

    X_train = pd.concat([X_train, exog_to_train, X_levels], axis=1)
    self.X_train_col_names = X_train.columns.to_list()

    y_train = pd.Series(
                  data = y_train,
                  name = 'y'
              )

    _, y_index = preprocess_y(y=series, return_values=False)

    y_index_numpy = y_index.to_numpy()
    y_train_index = pd.Index(
                        np.concatenate(
                            [y_index_numpy[-length:, ] for length in len_series]
                        )
                    )

    return X_train, y_train, y_index, y_train_index

create_sample_weights(series, X_train, y_train_index)

Crate weights for each observation according to the forecaster's attributes series_weights and weight_func. The resulting weights are product of both types of weights.

Parameters:

Name	Type	Description	Default
series	pandas DataFrame	Time series used to create X_train with the method create_train_X_y.	required
X_train	pandas DataFrame	Dataframe created with the create_train_X_y method, first return.	required
y_train_index	pandas Index	Index created with the create_train_X_y method, fourth return.	required

Returns:

Name	Type	Description
weights	numpy ndarray	Weights to use in fit method.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def create_sample_weights(
    self,
    series: pd.DataFrame,
    X_train: pd.DataFrame,
    y_train_index: pd.Index,
)-> np.ndarray:
    """
    Crate weights for each observation according to the forecaster's attributes
    `series_weights` and `weight_func`. The resulting weights are product of both
    types of weights.

    Parameters
    ----------
    series : pandas DataFrame
        Time series used to create `X_train` with the method `create_train_X_y`.
    X_train : pandas DataFrame
        Dataframe created with the `create_train_X_y` method, first return.
    y_train_index : pandas Index
        Index created with the `create_train_X_y` method, fourth return.

    Returns
    -------
    weights : numpy ndarray
        Weights to use in `fit` method.

    """

    weights = None
    weights_samples = None
    weights_series = None

    if self.series_weights is not None:
        # Series not present in series_weights have a weight of 1 in all their samples.
        # Keys in series_weights not present in series are ignored.
        series_not_in_series_weights = set(series.columns) - set(self.series_weights.keys())
        if series_not_in_series_weights:
            warnings.warn(
                (f"{series_not_in_series_weights} not present in `series_weights`."
                 f" A weight of 1 is given to all their samples."),
                IgnoredArgumentWarning
            )
        self.series_weights_ = {col: 1. for col in series.columns}
        self.series_weights_.update((k, v) for k, v in self.series_weights.items() 
                                    if k in self.series_weights_)
        weights_series = [np.repeat(self.series_weights_[serie], sum(X_train[serie])) 
                          for serie in series.columns]
        weights_series = np.concatenate(weights_series)

    if self.weight_func is not None:
        if isinstance(self.weight_func, Callable):
            self.weight_func_ = {col: copy(self.weight_func) 
                                 for col in series.columns}
        else:
            # Series not present in weight_func have a weight of 1 in all their samples
            series_not_in_weight_func = set(series.columns) - set(self.weight_func.keys())
            if series_not_in_weight_func:
                warnings.warn(
                    (f"{series_not_in_weight_func} not present in `weight_func`."
                     f" A weight of 1 is given to all their samples."),
                    IgnoredArgumentWarning
                )
            self.weight_func_ = {col: lambda x: np.ones_like(x, dtype=float) 
                                 for col in series.columns}
            self.weight_func_.update((k, v) for k, v in self.weight_func.items() 
                                     if k in self.weight_func_)

        weights_samples = []
        for key in self.weight_func_.keys():
            idx = y_train_index[X_train[X_train[key] == 1.0].index]
            weights_samples.append(self.weight_func_[key](idx))
        weights_samples = np.concatenate(weights_samples)

    if weights_series is not None:
        weights = weights_series
        if weights_samples is not None:
            weights = weights * weights_samples
    else:
        if weights_samples is not None:
            weights = weights_samples

    if weights is not None:
        if np.isnan(weights).any():
            raise ValueError(
                "The resulting `weights` cannot have NaN values."
            )
        if np.any(weights < 0):
            raise ValueError(
                "The resulting `weights` cannot have negative values."
            )
        if np.sum(weights) == 0:
            raise ValueError(
                ("The resulting `weights` cannot be normalized because "
                 "the sum of the weights is zero.")
            )

    return weights

fit(series, exog=None, store_in_sample_residuals=True)

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
series	pandas DataFrame	Training time series.	required
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s. Must have the same number of observations as series and their indexes must be aligned so that series[i] is regressed on exog[i].	`None`
store_in_sample_residuals	bool	If True, in-sample residuals will be stored in the forecaster object after fitting.	`True`

Returns:

Type	Description
None

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def fit(
    self,
    series: pd.DataFrame,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    store_in_sample_residuals: bool=True
) -> 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
    ----------
    series : pandas DataFrame
        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 `series` and their indexes must be aligned so
        that series[i] is regressed on exog[i].
    store_in_sample_residuals : bool, default `True`
        If `True`, in-sample residuals will be stored in the forecaster object
        after fitting.

    Returns
    -------
    None

    """

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

    self.series_col_names = list(series.columns)

    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]

        if len(set(self.exog_col_names) - set(self.series_col_names)) != len(self.exog_col_names):
            raise ValueError(
                (f"`exog` cannot contain a column named the same as one of the "
                 f"series (column names of series).\n"
                 f"    `series` columns : {self.series_col_names}.\n"
                 f"    `exog`   columns : {self.exog_col_names}.")
            )

    X_train, y_train, y_index, y_train_index = self.create_train_X_y(series=series, exog=exog)
    sample_weight = self.create_sample_weights(
                        series        = series,
                        X_train       = X_train,
                        y_train_index = y_train_index,
                    )

    if sample_weight is not None:
        self.regressor.fit(
            X             = X_train,
            y             = y_train,
            sample_weight = sample_weight,
            **self.fit_kwargs
        )
    else:
        self.regressor.fit(X=X_train, y=y_train, **self.fit_kwargs)

    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
    self.index_values = y_index

    in_sample_residuals = {}

    # This is done to save time during fit in functions such as backtesting()
    if store_in_sample_residuals:

        residuals = y_train - self.regressor.predict(X_train)

        for serie in series.columns:
            in_sample_residuals[serie] = residuals.loc[X_train[serie] == 1.].to_numpy()
            if len(in_sample_residuals[serie]) > 1000:
                # Only up to 1000 residuals are stored
                rng = np.random.default_rng(seed=123)
                in_sample_residuals[serie] = rng.choice(
                                                 a       = in_sample_residuals[serie], 
                                                 size    = 1000, 
                                                 replace = False
                                             )
    else:
        for serie in series.columns:
            in_sample_residuals[serie] = np.array([None])

    self.in_sample_residuals = in_sample_residuals

    # The last time window of training data is stored so that predictors in
    # the first iteration of `predict()` can be calculated.
    self.last_window = series.iloc[-self.window_size:].copy()

_recursive_predict(steps, level, last_window, exog=None)

Predict n steps ahead. It is an iterative process in which, each prediction, is used as a predictor for the next step.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
level	str	Time series to be predicted.	required
last_window	numpy ndarray	Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1).	required
exog	numpy ndarray	Exogenous variable/s included as predictor/s.	`None`

Returns:

Name	Type	Description
predictions	numpy ndarray	Predicted values.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def _recursive_predict(
    self,
    steps: int,
    level: str,
    last_window: np.ndarray,
    exog: Optional[np.ndarray]=None
) -> np.ndarray:
    """
    Predict n steps ahead. It is an iterative process in which, each prediction,
    is used as a predictor for the next step.

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    level : str
        Time series to be predicted.
    last_window : numpy ndarray
        Series values used to create the predictors (lags) needed in the 
        first iteration of the prediction (t + 1).
    exog : numpy ndarray, default `None`
        Exogenous variable/s included as predictor/s.

    Returns
    -------
    predictions : numpy ndarray
        Predicted values.

    """

    predictions = np.full(shape=steps, fill_value=np.nan)

    for i in range(steps):
        X = self.fun_predictors(y=last_window).reshape(1, -1)
        if exog is not None:
            X = np.column_stack((X, exog[i, ].reshape(1, -1)))

        levels_dummies = np.zeros(shape=(1, len(self.series_col_names)), dtype=float)
        levels_dummies[0][self.series_col_names.index(level)] = 1.

        X = np.column_stack((X, levels_dummies.reshape(1, -1)))

        with warnings.catch_warnings():
            # Suppress scikit-learn warning: "X does not have valid feature names,
            # but NoOpTransformer was fitted with feature names".
            warnings.simplefilter("ignore")
            prediction = self.regressor.predict(X)
            predictions[i] = prediction.ravel()[0]

        # Update `last_window` values. The first position is discarded and 
        # the new prediction is added at the end.
        last_window = np.append(last_window[1:], prediction)

    return predictions

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

Predict n steps ahead. It is an recursive process in which, each prediction, is used as a predictor for the next step.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
levels	str, list	Time series to be predicted. If None all levels will be predicted.	`None`
last_window	pandas DataFrame	Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1). If last_window = None, the values stored in self.last_window are used to calculate the initial predictors, and the predictions start right after training data.	`None`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s.	`None`

Returns:

Name	Type	Description
predictions	pandas DataFrame	Predicted values, one column for each level.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def predict(
    self,
    steps: int,
    levels: Optional[Union[str, list]]=None,
    last_window: Optional[pd.DataFrame]=None,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None
) -> pd.DataFrame:
    """
    Predict n steps ahead. It is an recursive process in which, each prediction,
    is used as a predictor for the next step.

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    levels : str, list, default `None`
        Time series to be predicted. If `None` all levels will be predicted.
    last_window : pandas DataFrame, default `None`
        Series values used to create the predictors (lags) needed in the 
        first iteration of the prediction (t + 1).
        If `last_window = None`, the values stored in `self.last_window` are
        used to calculate the initial predictors, and the predictions start
        right after training data.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s.

    Returns
    -------
    predictions : pandas DataFrame
        Predicted values, one column for each level.

    """

    if levels is None:
        levels = self.series_col_names
    elif isinstance(levels, str):
        levels = [levels]

    if last_window is None:
        last_window = deepcopy(self.last_window)

    last_window = last_window.iloc[-self.window_size:, ]

    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,
        exog             = exog,
        exog_type        = self.exog_type,
        exog_col_names   = self.exog_col_names,
        interval         = None,
        max_steps        = None,
        levels           = levels,
        series_col_names = self.series_col_names
    )

    if exog is not None:
        if isinstance(exog, pd.DataFrame):
            exog = transform_dataframe(
                       df                = exog,
                       transformer       = self.transformer_exog,
                       fit               = False,
                       inverse_transform = False
                   )
        else:
            exog = transform_series(
                       series            = exog,
                       transformer       = self.transformer_exog,
                       fit               = False,
                       inverse_transform = False
                   )
        check_exog_dtypes(exog=exog)
        exog_values = exog.to_numpy()[:steps]
    else:
        exog_values = None

    predictions = []

    for level in levels:

        last_window_level = transform_series(
                                series            = last_window[level],
                                transformer       = self.transformer_series_[level],
                                fit               = False,
                                inverse_transform = False
                            )
        last_window_values, last_window_index = preprocess_last_window(
                                                    last_window = last_window_level
                                                )

        preds_level = self._recursive_predict(
                          steps       = steps,
                          level       = level,
                          last_window = copy(last_window_values),
                          exog        = copy(exog_values)
                      )

        preds_level = pd.Series(
                          data  = preds_level,
                          index = expand_index(
                                      index = last_window_index,
                                      steps = steps
                                  ),
                          name = level
                      )

        preds_level = transform_series(
                          series            = preds_level,
                          transformer       = self.transformer_series_[level],
                          fit               = False,
                          inverse_transform = True
                      )

        predictions.append(preds_level)    

    predictions = pd.concat(predictions, axis=1)

    return predictions

predict_bootstrapping(steps, levels=None, last_window=None, exog=None, n_boot=500, random_state=123, in_sample_residuals=True)

Generate multiple forecasting predictions using a bootstrapping process. By sampling from a collection of past observed errors (the residuals), each iteration of bootstrapping generates a different set of predictions. See the Notes section for more information.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
levels	str, list	Time series to be predicted. If None all levels will be predicted.	`None`
last_window	pandas DataFrame	Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1). If last_window = None, the values stored in self.last_window are used to calculate the initial predictors, and the predictions start right after training data.	`None`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s.	`None`
n_boot	int	Number of bootstrapping iterations used to estimate prediction intervals.	`500`
random_state	int	Sets a seed to the random generator, so that boot intervals are always deterministic.	`123`
in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create prediction intervals. If False, out of sample residuals are used. In the latter case, the user should have calculated and stored the residuals within the forecaster (see set_out_sample_residuals()).	`True`

Returns:

Name	Type	Description
boot_predictions	dict	Predictions generated by bootstrapping for each level. {level: pandas DataFrame, shape (steps, n_boot)}

Notes

More information about prediction intervals in forecasting: https://otexts.com/fpp3/prediction-intervals.html#prediction-intervals-from-bootstrapped-residuals Forecasting: Principles and Practice (3nd ed) Rob J Hyndman and George Athanasopoulos.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def predict_bootstrapping(
    self,
    steps: int,
    levels: Optional[Union[str, list]]=None,
    last_window: Optional[pd.DataFrame]=None,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    n_boot: int=500,
    random_state: int=123,
    in_sample_residuals: bool=True
) -> dict:
    """
    Generate multiple forecasting predictions using a bootstrapping process. 
    By sampling from a collection of past observed errors (the residuals),
    each iteration of bootstrapping generates a different set of predictions. 
    See the Notes section for more information. 

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    levels : str, list, default `None`
        Time series to be predicted. If `None` all levels will be predicted.
    last_window : pandas DataFrame, default `None`
        Series values used to create the predictors (lags) needed in the 
        first iteration of the prediction (t + 1).
        If `last_window = None`, the values stored in `self.last_window` are
        used to calculate the initial predictors, and the predictions start
        right after training data.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s.
    n_boot : int, default `500`
        Number of bootstrapping iterations used to estimate prediction
        intervals.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot intervals are always 
        deterministic.        
    in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of
        prediction error to create prediction intervals. If `False`, out of
        sample residuals are used. In the latter case, the user should have
        calculated and stored the residuals within the forecaster (see
        `set_out_sample_residuals()`).

    Returns
    -------
    boot_predictions : dict
        Predictions generated by bootstrapping for each level. 
        {level: pandas DataFrame, shape (steps, n_boot)}

    Notes
    -----
    More information about prediction intervals in forecasting:
    https://otexts.com/fpp3/prediction-intervals.html#prediction-intervals-from-bootstrapped-residuals
    Forecasting: Principles and Practice (3nd ed) Rob J Hyndman and George Athanasopoulos.

    """

    if levels is None:
        levels = self.series_col_names
    elif isinstance(levels, str):
        levels = [levels]

    if in_sample_residuals:
        if not set(levels).issubset(set(self.in_sample_residuals.keys())):
            raise ValueError(
                (f"Not `forecaster.in_sample_residuals` for levels: "
                 f"{set(levels) - set(self.in_sample_residuals.keys())}.")
            )
        residuals_levels = self.in_sample_residuals
    else:
        if self.out_sample_residuals is None:
            raise ValueError(
                ("`forecaster.out_sample_residuals` is `None`. Use "
                 "`in_sample_residuals=True` or method "
                 "`set_out_sample_residuals()` before `predict_interval()`, "
                 "`predict_bootstrapping()` or `predict_dist()`.")
            )
        else:
            if not set(levels).issubset(set(self.out_sample_residuals.keys())):
                raise ValueError(
                    (f"Not `forecaster.out_sample_residuals` for levels: "
                     f"{set(levels) - set(self.out_sample_residuals.keys())}. "
                     f"Use method `set_out_sample_residuals()`.")
                )
        residuals_levels = self.out_sample_residuals

    check_residuals = (
        "forecaster.in_sample_residuals" if in_sample_residuals
         else "forecaster.out_sample_residuals"
    )
    for level in levels:
        if residuals_levels[level] is None:
            raise ValueError(
                (f"forecaster residuals for level '{level}' are `None`. "
                 f"Check `{check_residuals}`.")
            )
        elif (residuals_levels[level] == None).any():
            raise ValueError(
                (f"forecaster residuals for level '{level}' contains `None` "
                 f"values. Check `{check_residuals}`.")
            )

    if last_window is None:
        last_window = deepcopy(self.last_window)

    last_window = last_window.iloc[-self.window_size:, ]

    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,
        last_window_exog = None,
        exog             = exog,
        exog_type        = self.exog_type,
        exog_col_names   = self.exog_col_names,
        interval         = None,
        alpha            = None,
        max_steps        = None,
        levels           = levels,
        series_col_names = self.series_col_names
    )

    if exog is not None:
        if isinstance(exog, pd.DataFrame):
            exog = transform_dataframe(
                       df                = exog,
                       transformer       = self.transformer_exog,
                       fit               = False,
                       inverse_transform = False
                   )
        else:
            exog = transform_series(
                       series            = exog,
                       transformer       = self.transformer_exog,
                       fit               = False,
                       inverse_transform = False
                   )

        exog_values = exog.to_numpy()[:steps]
    else:
        exog_values = None

    boot_predictions = {}

    for level in levels:

        last_window_level = transform_series(
                                series            = last_window[level],
                                transformer       = self.transformer_series_[level],
                                fit               = False,
                                inverse_transform = False
                            )
        last_window_values, last_window_index = preprocess_last_window(
                                                    last_window = last_window_level
                                                )

        level_boot_predictions = np.full(
                                     shape      = (steps, n_boot),
                                     fill_value = np.nan,
                                     dtype      = float
                                 )
        rng = np.random.default_rng(seed=random_state)
        seeds = rng.integers(low=0, high=10000, size=n_boot)

        residuals = residuals_levels[level]

        for i in range(n_boot):
            # In each bootstraping iteration the initial last_window and exog 
            # need to be restored.
            last_window_boot = last_window_values.copy()
            exog_boot = exog_values.copy() if exog is not None else None

            rng = np.random.default_rng(seed=seeds[i])
            sample_residuals = rng.choice(
                                   a       = residuals,
                                   size    = steps,
                                   replace = True
                               )

            for step in range(steps):

                prediction = self._recursive_predict(
                                 steps       = 1,
                                 level       = level,
                                 last_window = last_window_boot,
                                 exog        = exog_boot 
                             )

                prediction_with_residual = prediction + sample_residuals[step]
                level_boot_predictions[step, i] = prediction_with_residual

                last_window_boot = np.append(
                                       last_window_boot[1:],
                                       prediction_with_residual
                                   )
                if exog is not None:
                    exog_boot = exog_boot[1:]

        level_boot_predictions = pd.DataFrame(
                                     data    = level_boot_predictions,
                                     index   = expand_index(last_window_index, steps=steps),
                                     columns = [f"pred_boot_{i}" for i in range(n_boot)]
                                 )

        if self.transformer_series_[level]:
            for col in level_boot_predictions.columns:
                level_boot_predictions[col] = transform_series(
                                                  series            = level_boot_predictions[col],
                                                  transformer       = self.transformer_series_[level],
                                                  fit               = False,
                                                  inverse_transform = True
                                              )

        boot_predictions[level] = level_boot_predictions

    return boot_predictions

predict_interval(steps, levels=None, last_window=None, exog=None, interval=[5, 95], n_boot=500, random_state=123, in_sample_residuals=True)

Iterative process in which, each prediction, is used as a predictor for the next step and bootstrapping is used to estimate prediction intervals. Both predictions and intervals are returned.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
levels	str, list	Time series to be predicted. If None all levels will be predicted.	`None`
last_window	pandas DataFrame	Series values used to create the predictors (lags) needed in the first iteration of the prediction (t + 1). If last_window = None, the values stored inself.last_window are used to calculate the initial predictors, and the predictions start right after training data.	`None`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s.	`None`
interval	list	Confidence of the prediction interval estimated. 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].	`[5, 95]`
n_boot	int	Number of bootstrapping iterations used to estimate prediction intervals.	`500`
random_state	int	Sets a seed to the random generator, so that boot intervals are always deterministic.	`123`
in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create prediction intervals. If False, out of sample residuals are used. In the latter case, the user should have calculated and stored the residuals within the forecaster (see set_out_sample_residuals()).	`True`

Returns:

Name	Type	Description
predictions	pandas DataFrame	Values predicted by the forecaster and their estimated interval. level: predictions. level_lower_bound: lower bound of the interval. level_upper_bound: upper bound of the interval.

Notes

More information about prediction intervals in forecasting: https://otexts.com/fpp2/prediction-intervals.html Forecasting: Principles and Practice (2nd ed) Rob J Hyndman and George Athanasopoulos.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def predict_interval(
    self,
    steps: int,
    levels: Optional[Union[str, list]]=None,
    last_window: Optional[pd.DataFrame]=None,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    interval: list=[5, 95],
    n_boot: int=500,
    random_state: int=123,
    in_sample_residuals: bool=True
) -> pd.DataFrame:
    """
    Iterative process in which, each prediction, is used as a predictor
    for the next step and bootstrapping is used to estimate prediction
    intervals. Both predictions and intervals are returned.

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    levels : str, list, default `None`
        Time series to be predicted. If `None` all levels will be predicted.  
    last_window : pandas DataFrame, default `None`
        Series values used to create the predictors (lags) needed in the 
        first iteration of the prediction (t + 1).
        If `last_window = None`, the values stored in` self.last_window` are
        used to calculate the initial predictors, and the predictions start
        right after training data.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s.
    interval : list, default `[5, 95]`
        Confidence of the prediction interval estimated. 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]`.
    n_boot : int, default `500`
        Number of bootstrapping iterations used to estimate prediction
        intervals.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot intervals are always 
        deterministic.
    in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of
        prediction error to create prediction intervals. If `False`, out of
        sample residuals are used. In the latter case, the user should have
        calculated and stored the residuals within the forecaster (see
        `set_out_sample_residuals()`).

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

            - level: predictions.
            - level_lower_bound: lower bound of the interval.
            - level_upper_bound: upper bound of the interval.

    Notes
    -----
    More information about prediction intervals in forecasting:
    https://otexts.com/fpp2/prediction-intervals.html
    Forecasting: Principles and Practice (2nd ed) Rob J Hyndman and
    George Athanasopoulos.

    """

    if levels is None:
        levels = self.series_col_names
    elif isinstance(levels, str):
        levels = [levels]

    check_interval(interval=interval)

    preds = self.predict(
                steps       = steps,
                levels      = levels,
                last_window = last_window,
                exog        = exog
            )

    boot_predictions = self.predict_bootstrapping(
                           steps               = steps,
                           levels              = levels,
                           last_window         = last_window,
                           exog                = exog,
                           n_boot              = n_boot,
                           random_state        = random_state,
                           in_sample_residuals = in_sample_residuals
                       )

    interval = np.array(interval)/100
    predictions = []

    for level in levels:
        preds_interval = boot_predictions[level].quantile(q=interval, axis=1).transpose()
        preds_interval.columns = [f'{level}_lower_bound', f'{level}_upper_bound']
        predictions.append(preds[level])
        predictions.append(preds_interval)

    predictions = pd.concat(predictions, axis=1)

    return predictions

predict_dist(steps, distribution, levels=None, last_window=None, exog=None, n_boot=500, random_state=123, in_sample_residuals=True)

Fit a given probability distribution for each step. After generating multiple forecasting predictions through a bootstrapping process, each step is fitted to the given distribution.

Parameters:

Name	Type	Description	Default
steps	int	Number of future steps predicted.	required
distribution	Object	A distribution object from scipy.stats. For example scipy.stats.norm.	required
levels	str, list	Time series to be predicted. If None all levels will be predicted.	`None`
last_window	pandas DataFrame	Values of the series used to create the predictors needed in the first re of prediction (t + 1). If last_window = None, the values stored inself.last_window are used to calculate the initial predictors, and the predictions start right after training data.	`None`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s.	`None`
n_boot	int	Number of bootstrapping iterations used to estimate prediction intervals.	`500`
random_state	int	Sets a seed to the random generator, so that boot intervals are always deterministic.	`123`
in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create prediction intervals. If False, out of sample residuals are used. In the latter case, the user should have calculated and stored the residuals within the forecaster (see set_out_sample_residuals()).	`True`

Returns:

Name	Type	Description
predictions	pandas DataFrame	Distribution parameters estimated for each step and level.

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def predict_dist(
    self,
    steps: int,
    distribution: object,
    levels: Optional[Union[str, list]]=None,
    last_window: Optional[pd.DataFrame]=None,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    n_boot: int=500,
    random_state: int=123,
    in_sample_residuals: bool=True
) -> pd.DataFrame:
    """
    Fit a given probability distribution for each step. After generating 
    multiple forecasting predictions through a bootstrapping process, each 
    step is fitted to the given distribution.

    Parameters
    ----------
    steps : int
        Number of future steps predicted.
    distribution : Object
        A distribution object from scipy.stats. For example scipy.stats.norm.
    levels : str, list, default `None`
        Time series to be predicted. If `None` all levels will be predicted.  
    last_window : pandas DataFrame, default `None`
        Values of the series used to create the predictors needed in the first
        re of prediction (t + 1).
        If `last_window = None`, the values stored in` self.last_window` are
        used to calculate the initial predictors, and the predictions start
        right after training data.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s.
    n_boot : int, default `500`
        Number of bootstrapping iterations used to estimate prediction
        intervals.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot intervals are always 
        deterministic.
    in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of
        prediction error to create prediction intervals. If `False`, out of
        sample residuals are used. In the latter case, the user should have
        calculated and stored the residuals within the forecaster (see
        `set_out_sample_residuals()`).

    Returns
    -------
    predictions : pandas DataFrame
        Distribution parameters estimated for each step and level.

    """

    if levels is None:
        levels = self.series_col_names
    elif isinstance(levels, str):
        levels = [levels]

    boot_samples = self.predict_bootstrapping(
                       steps               = steps,
                       levels              = levels,
                       last_window         = last_window,
                       exog                = exog,
                       n_boot              = n_boot,
                       random_state        = random_state,
                       in_sample_residuals = in_sample_residuals
                   )

    param_names = [p for p in inspect.signature(distribution._pdf).parameters if not p=='x'] + ["loc","scale"]
    predictions = []

    for level in levels:
        param_values = np.apply_along_axis(lambda x: distribution.fit(x), axis=1, arr=boot_samples[level])
        level_param_names = [f'{level}_{p}' for p in param_names]

        pred_level = pd.DataFrame(
                         data    = param_values,
                         columns = level_param_names,
                         index   = boot_samples[level].index
                     )

        predictions.append(pred_level)

    predictions = pd.concat(predictions, axis=1)

    return predictions

set_params(params)

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

Parameters:

Name	Type	Description	Default
params	dict	Parameters values.	required

Returns:

Type	Description
None

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

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

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

    Returns
    -------
    None

    """

    self.regressor = clone(self.regressor)
    self.regressor.set_params(**params)

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\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.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

    """

    self.fit_kwargs = check_select_fit_kwargs(self.regressor, fit_kwargs=fit_kwargs)

set_out_sample_residuals(residuals, append=True, transform=True, random_state=123)

Set new values to the attribute out_sample_residuals. Out of sample residuals are meant to be calculated using observations that did not participate in the training process.

Parameters:

Name	Type	Description	Default
residuals	dict	Dictionary of numpy ndarrays with the residuals of each level in the form {level: residuals}. If len(residuals) > 1000, only a random sample of 1000 values are stored. Keys must be the same as levels.	required
append	bool	If True, new residuals are added to the once already stored in the attribute out_sample_residuals. Once the limit of 1000 values is reached, no more values are appended. If False, out_sample_residuals is overwritten with the new residuals.	`True`
transform	bool	If True, new residuals are transformed using self.transformer_series.	`True`
random_state	int	Sets a seed to the random sampling for reproducible output.	`123`

Returns:

Type	Description
None

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def set_out_sample_residuals(
    self, 
    residuals: dict,
    append: bool=True,
    transform: bool=True,
    random_state: int=123
)-> None:
    """
    Set new values to the attribute `out_sample_residuals`. Out of sample
    residuals are meant to be calculated using observations that did not
    participate in the training process.

    Parameters
    ----------
    residuals : dict
        Dictionary of numpy ndarrays with the residuals of each level in the
        form {level: residuals}. If len(residuals) > 1000, only a random 
        sample of 1000 values are stored. Keys must be the same as `levels`.
    append : bool, default `True`
        If `True`, new residuals are added to the once already stored in the
        attribute `out_sample_residuals`. Once the limit of 1000 values is
        reached, no more values are appended. If False, `out_sample_residuals`
        is overwritten with the new residuals.
    transform : bool, default `True`
        If `True`, new residuals are transformed using self.transformer_series.
    random_state : int, default `123`
        Sets a seed to the random sampling for reproducible output.

    Returns
    -------
    None

    """

    if not isinstance(residuals, dict) or not all(isinstance(x, np.ndarray) for x in residuals.values()):
        raise TypeError(
            (f"`residuals` argument must be a dict of numpy ndarrays in the form "
             "`{level: residuals}`. " 
             f"Got {type(residuals)}.")
        )

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

    if self.out_sample_residuals is None:
        self.out_sample_residuals = {level: None for level in self.series_col_names}

    if not set(self.out_sample_residuals.keys()).issubset(set(residuals.keys())):
        warnings.warn(
            (f"""
            Only residuals of levels 
            {set(self.out_sample_residuals.keys()).intersection(set(residuals.keys()))} 
            are updated.
            """), IgnoredArgumentWarning
        )

    residuals = {key: value 
                 for key, value in residuals.items() 
                 if key in self.out_sample_residuals.keys()}

    for level, value in residuals.items():

        residuals_level = value

        if not transform and self.transformer_series_[level] is not None:
            warnings.warn(
                ("Argument `transform` is set to `False` but forecaster was "
                f"trained using a transformer {self.transformer_series_[level]} "
                f"for level {level}. Ensure that the new residuals are "
                 "already transformed or set `transform=True`.")
            )

        if transform and self.transformer_series_ and self.transformer_series_[level]:
            warnings.warn(
                ("Residuals will be transformed using the same transformer used "
                f"when training the forecaster for level {level} : "
                f"({self.transformer_series_[level]}). Ensure that the new "
                 "residuals are on the same scale as the original time series.")
            )
            residuals_level = transform_series(
                                  series            = pd.Series(residuals_level, name='residuals'),
                                  transformer       = self.transformer_series_[level],
                                  fit               = False,
                                  inverse_transform = False
                              ).to_numpy()

        if len(residuals_level) > 1000:
            rng = np.random.default_rng(seed=random_state)
            residuals_level = rng.choice(a=residuals_level, size=1000, replace=False)

        if append and self.out_sample_residuals[level] is not None:
            free_space = max(0, 1000 - len(self.out_sample_residuals[level]))
            if len(residuals_level) < free_space:
                residuals_level = np.hstack((
                                        self.out_sample_residuals[level],
                                        residuals_level
                                    ))
            else:
                residuals_level = np.hstack((
                                        self.out_sample_residuals[level],
                                        residuals_level[:free_space]
                                    ))

        self.out_sample_residuals[level] = residuals_level

get_feature_importances()

Return feature importances of the regressor stored in the forecaster. Only valid when regressor stores internally the feature importances in the attribute feature_importances_ or coef_.

Parameters:

Name	Type	Description	Default
self			required

Returns:

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

Source code in skforecast\ForecasterAutoregMultiSeriesCustom\ForecasterAutoregMultiSeriesCustom.py

def get_feature_importances(
    self
) -> pd.DataFrame:
    """
    Return feature importances of the regressor stored in the
    forecaster. Only valid when regressor stores internally the feature
    importances in the attribute `feature_importances_` or `coef_`.

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

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

    """

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

    if isinstance(self.regressor, sklearn.pipeline.Pipeline):
        estimator = self.regressor[-1]
    else:
        estimator = self.regressor

    if hasattr(estimator, 'feature_importances_'):
        feature_importances = estimator.feature_importances_
    elif hasattr(estimator, 'coef_'):
        feature_importances = estimator.coef_
    else:
        warnings.warn(
            (f"Impossible to access feature importances for regressor of type "
             f"{type(estimator)}. This method is only valid when the "
             f"regressor stores internally the feature importances in the "
             f"attribute `feature_importances_` or `coef_`.")
        )
        feature_importances = None

    if feature_importances is not None:
        feature_importances = pd.DataFrame({
                                  'feature': self.X_train_col_names,
                                  'importance': feature_importances
                              })

    return feature_importances