ForecasterDirect

skforecast.direct._forecaster_direct.ForecasterDirect

ForecasterDirect(
    regressor,
    steps,
    lags=None,
    window_features=None,
    transformer_y=None,
    transformer_exog=None,
    weight_func=None,
    differentiation=None,
    fit_kwargs=None,
    n_jobs="auto",
    forecaster_id=None,
)

Bases: ForecasterBase

This class turns any regressor compatible with the scikit-learn API into a autoregressive direct multi-step forecaster. A separate model is created for each forecast time step. See documentation for more details.

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
steps	int	Maximum number of future steps the forecaster will predict when using method predict(). Since a different model is created for each step, this value should be defined before training.	required
lags	int, list, numpy ndarray, range	Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1. int: include lags from 1 to lags (included). list, 1d numpy ndarray or range: include only lags present in lags, all elements must be int. None: no lags are included as predictors.	`None`
window_features	(object, list)	Instance or list of instances used to create window features. Window features are created from the original time series and are included as predictors.	`None`
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`
weight_func	Callable	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. The resulting sample_weight cannot have negative values.	`None`
differentiation	int	Order of differencing applied to the time series before training the forecaster. If None, no differencing is applied. The order of differentiation is the number of times the differencing operation is applied to a time series. Differencing involves computing the differences between consecutive data points in the series. Differentiation is reversed in the output of predict() and predict_interval(). WARNING: This argument is newly introduced and requires special attention. It is still experimental and may undergo changes.	`None`
fit_kwargs	dict	Additional arguments to be passed to the fit method of the regressor.	`None`
n_jobs	(int, auto)	The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores. If 'auto', n_jobs is set using the function skforecast.utils.select_n_jobs_fit_forecaster.	`'auto'`
forecaster_id	(str, int)	Name used as an identifier of the forecaster.	`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. An instance of this regressor is trained for each step. All of them are stored in self.regressors_.
regressors_	dict	Dictionary with regressors trained for each step. They are initialized as a copy of regressor.
steps	int	Number of future steps the forecaster will predict when using method predict(). Since a different model is created for each step, this value should be defined before training.
lags	numpy ndarray	Lags used as predictors.
lags_names	list	Names of the lags used as predictors.
max_lag	int	Maximum lag included in lags.
window_features	list	Class or list of classes used to create window features.
window_features_names	list	Names of the window features to be included in the X_train matrix.
window_features_class_names	list	Names of the classes used to create the window features.
max_size_window_features	int	Maximum window size required by the window features.
window_size	int	The window size needed to create the predictors. It is calculated as the maximum value between max_lag and max_size_window_features. If differentiation is used, window_size is increased by n units equal to the order of differentiation so that predictors can be generated correctly.
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.
weight_func	Callable	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. The resulting sample_weight cannot have negative values.
source_code_weight_func	str	Source code of the custom function used to create weights.
differentiation	int	Order of differencing applied to the time series before training the forecaster.
differentiator	TimeSeriesDifferentiator	Skforecast object used to differentiate the time series.
last_window_	pandas DataFrame	This window represents the most recent data observed by the predictor during its training phase. It contains the values needed to predict the next step immediately after the training data. These values are stored in the original scale of the time series before undergoing any transformations or differentiation. When differentiation parameter is specified, the dimensions of the last_window_ are expanded as many values as the order of differentiation. For example, if lags = 7 and differentiation = 1, last_window_ will have 8 values.
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.
exog_in_	bool	If the forecaster has been trained using exogenous variable/s.
exog_names_in_	list	Names of the exogenous variables used during training.
exog_type_in_	type	Type of exogenous data (pandas Series or DataFrame) used in training.
exog_dtypes_in_	dict	Type of each exogenous variable/s used in training. If transformer_exog is used, the dtypes are calculated after the transformation.
X_train_window_features_names_out_	list	Names of the window features included in the matrix X_train created internally for training.
X_train_exog_names_out_	list	Names of the exogenous variables included in the matrix X_train created internally for training. It can be different from exog_names_in_ if some exogenous variables are transformed during the training process.
X_train_direct_exog_names_out_	list	Same as X_train_exog_names_out_ but using the direct format. The same exogenous variable is repeated for each step.
X_train_features_names_out_	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.
in_sample_residuals_	dict	Residuals of the models when predicting training data. Only stored up to 1000 values per model in the form {step: residuals}. If transformer_y is not None, residuals are stored in the transformed scale.
out_sample_residuals_	dict	Residuals of the models when predicting non training data. Only stored up to 1000 values per model in the form {step: residuals}. If transformer_y is not None, residuals are assumed to be in the transformed scale. Use set_out_sample_residuals() method to set values.
creation_date	str	Date of creation.
is_fitted	bool	Tag to identify if the regressor has been fitted (trained).
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.
n_jobs	(int, auto)	The number of jobs to run in parallel. If -1, then the number of jobs is set to the number of cores. If 'auto', n_jobs is set using the function skforecast.utils.select_n_jobs_fit_forecaster.
forecaster_id	(str, int)	Name used as an identifier of the forecaster.
binner	Ignored	Not used, present here for API consistency by convention.
binner_intervals_	Ignored	Not used, present here for API consistency by convention.
binner_kwargs	Ignored	Not used, present here for API consistency by convention.
in_sample_residuals_by_bin_	Ignored	Not used, present here for API consistency by convention.
out_sample_residuals_by_bin_	Ignored	Not used, present here for API consistency by convention.

Notes

A separate model is created for each forecasting time step. It is important to note that all models share the same parameter and hyperparameter configuration.

Source code in skforecast\direct\_forecaster_direct.py

def __init__(
    self,
    regressor: object,
    steps: int,
    lags: Optional[Union[int, list, np.ndarray, range]] = None,
    window_features: Optional[Union[object, list]] = None,
    transformer_y: Optional[object] = None,
    transformer_exog: Optional[object] = None,
    weight_func: Optional[Callable] = None,
    differentiation: Optional[int] = None,
    fit_kwargs: Optional[dict] = None,
    n_jobs: Union[int, str] = 'auto',
    forecaster_id: Optional[Union[str, int]] = None
) -> None:

    self.regressor                          = copy(regressor)
    self.steps                              = steps
    self.transformer_y                      = transformer_y
    self.transformer_exog                   = transformer_exog
    self.weight_func                        = weight_func
    self.source_code_weight_func            = None
    self.differentiation                    = differentiation
    self.differentiator                     = None
    self.last_window_                       = None
    self.index_type_                        = None
    self.index_freq_                        = None
    self.training_range_                    = None
    self.exog_in_                           = False
    self.exog_names_in_                     = None
    self.exog_type_in_                      = None
    self.exog_dtypes_in_                    = None
    self.X_train_window_features_names_out_ = None
    self.X_train_exog_names_out_            = None
    self.X_train_direct_exog_names_out_     = None
    self.X_train_features_names_out_        = None
    self.creation_date                      = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.is_fitted                          = False
    self.fit_date                           = None
    self.skforecast_version                 = skforecast.__version__
    self.python_version                     = sys.version.split(" ")[0]
    self.forecaster_id                      = forecaster_id
    self.binner                             = None
    self.binner_intervals_                  = None
    self.binner_kwargs                      = None
    self.in_sample_residuals_by_bin_        = None
    self.out_sample_residuals_by_bin_       = None

    if not isinstance(steps, int):
        raise TypeError(
            f"`steps` argument must be an int greater than or equal to 1. "
            f"Got {type(steps)}."
        )

    if steps < 1:
        raise ValueError(
            f"`steps` argument must be greater than or equal to 1. Got {steps}."
        )

    self.regressors_ = {step: clone(self.regressor) for step in range(1, steps + 1)}
    self.lags, self.lags_names, self.max_lag = initialize_lags(type(self).__name__, lags)
    self.window_features, self.window_features_names, self.max_size_window_features = (
        initialize_window_features(window_features)
    )
    if self.window_features is None and self.lags is None:
        raise ValueError(
            "At least one of the arguments `lags` or `window_features` "
            "must be different from None. This is required to create the "
            "predictors used in training the forecaster."
        )

    self.window_size = max(
        [ws for ws in [self.max_lag, self.max_size_window_features] 
         if ws is not None]
    )
    self.window_features_class_names = None
    if window_features is not None:
        self.window_features_class_names = [
            type(wf).__name__ for wf in self.window_features
        ]

    if self.differentiation is not None:
        if not isinstance(differentiation, int) or differentiation < 1:
            raise ValueError(
                f"Argument `differentiation` must be an integer equal to or "
                f"greater than 1. Got {differentiation}."
            )
        self.window_size += self.differentiation
        self.differentiator = TimeSeriesDifferentiator(
            order=self.differentiation, window_size=self.window_size
        )

    self.weight_func, self.source_code_weight_func, _ = initialize_weights(
        forecaster_name = type(self).__name__, 
        regressor       = regressor, 
        weight_func     = weight_func, 
        series_weights  = None
    )

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

    self.in_sample_residuals_ = {step: None for step in range(1, steps + 1)}
    self.out_sample_residuals_ = None

    if n_jobs == 'auto':
        self.n_jobs = select_n_jobs_fit_forecaster(
                          forecaster_name = type(self).__name__,
                          regressor       = self.regressor
                      )
    else:
        if not isinstance(n_jobs, int):
            raise TypeError(
                f"`n_jobs` must be an integer or `'auto'`. Got {type(n_jobs)}."
            )
        self.n_jobs = n_jobs if n_jobs > 0 else cpu_count()

_repr_html_

_repr_html_()

HTML representation of the object. The "General Information" section is expanded by default.

Source code in skforecast\direct\_forecaster_direct.py

def _repr_html_(self):
    """
    HTML representation of the object.
    The "General Information" section is expanded by default.
    """

    (
        params,
        _,
        _,
        exog_names_in_,
        _,
    ) = self._preprocess_repr(
            regressor      = self.regressor,
            exog_names_in_ = self.exog_names_in_
        )

    style, unique_id = self._get_style_repr_html(self.is_fitted)

    content = f"""
    <div class="container-{unique_id}">
        <h2>{type(self).__name__}</h2>
        <details open>
            <summary>General Information</summary>
            <ul>
                <li><strong>Regressor:</strong> {type(self.regressor).__name__}</li>
                <li><strong>Lags:</strong> {self.lags}</li>
                <li><strong>Window features:</strong> {self.window_features_names}</li>
                <li><strong>Window size:</strong> {self.window_size}</li>
                <li><strong>Maximum steps to predict:</strong> {self.steps}</li>
                <li><strong>Exogenous included:</strong> {self.exog_in_}</li>
                <li><strong>Weight function included:</strong> {self.weight_func is not None}</li>
                <li><strong>Differentiation order:</strong> {self.differentiation}</li>
                <li><strong>Creation date:</strong> {self.creation_date}</li>
                <li><strong>Last fit date:</strong> {self.fit_date}</li>
                <li><strong>Skforecast version:</strong> {self.skforecast_version}</li>
                <li><strong>Python version:</strong> {self.python_version}</li>
                <li><strong>Forecaster id:</strong> {self.forecaster_id}</li>
            </ul>
        </details>
        <details>
            <summary>Exogenous Variables</summary>
            <ul>
                {exog_names_in_}
            </ul>
        </details>
        <details>
            <summary>Data Transformations</summary>
            <ul>
                <li><strong>Transformer for y:</strong> {self.transformer_y}</li>
                <li><strong>Transformer for exog:</strong> {self.transformer_exog}</li>
            </ul>
        </details>
        <details>
            <summary>Training Information</summary>
            <ul>
                <li><strong>Training range:</strong> {self.training_range_.to_list() if self.is_fitted else 'Not fitted'}</li>
                <li><strong>Training index type:</strong> {str(self.index_type_).split('.')[-1][:-2] if self.is_fitted else 'Not fitted'}</li>
                <li><strong>Training index frequency:</strong> {self.index_freq_ if self.is_fitted else 'Not fitted'}</li>
            </ul>
        </details>
        <details>
            <summary>Regressor Parameters</summary>
            <ul>
                {params}
            </ul>
        </details>
        <details>
            <summary>Fit Kwargs</summary>
            <ul>
                {self.fit_kwargs}
            </ul>
        </details>
        <p>
            <a href="https://skforecast.org/{skforecast.__version__}/api/forecasterdirect.html">&#128712 <strong>API Reference</strong></a>
            &nbsp;&nbsp;
            <a href="https://skforecast.org/{skforecast.__version__}/user_guides/direct-multi-step-forecasting.html">&#128462 <strong>User Guide</strong></a>
        </p>
    </div>
    """

    # Return the combined style and content
    return style + content

_create_lags

_create_lags(y, X_as_pandas=False, train_index=None)

Create the lagged values and their target variable from a time series.

Note that the returned matrix X_data contains the lag 1 in the first column, the lag 2 in the in the second column and so on.

Parameters:

Name	Type	Description	Default
y	numpy ndarray	Training time series values.	required
X_as_pandas	bool	If True, the returned matrix X_data is a pandas DataFrame.	`False`
train_index	pandas Index	Index of the training data. It is used to create the pandas DataFrame X_data when X_as_pandas is True.	`None`

Returns:

Name	Type	Description
X_data	numpy ndarray, pandas DataFrame, None	Lagged values (predictors).
y_data	numpy ndarray	Values of the time series related to each row of X_data.

Source code in skforecast\direct\_forecaster_direct.py

def _create_lags(
    self, 
    y: np.ndarray,
    X_as_pandas: bool = False,
    train_index: Optional[pd.Index] = None
) -> Tuple[Optional[Union[np.ndarray, pd.DataFrame]], np.ndarray]:
    """
    Create the lagged values and their target variable from a time series.

    Note that the returned matrix `X_data` contains the lag 1 in the first 
    column, the lag 2 in the in the second column and so on.

    Parameters
    ----------
    y : numpy ndarray
        Training time series values.
    X_as_pandas : bool, default `False`
        If `True`, the returned matrix `X_data` is a pandas DataFrame.
    train_index : pandas Index, default `None`
        Index of the training data. It is used to create the pandas DataFrame
        `X_data` when `X_as_pandas` is `True`.

    Returns
    -------
    X_data : numpy ndarray, pandas DataFrame, None
        Lagged values (predictors).
    y_data : numpy ndarray
        Values of the time series related to each row of `X_data`.

    """

    n_rows = len(y) - self.window_size - (self.steps - 1)

    X_data = None
    if self.lags is not None:
        X_data = np.full(
            shape=(n_rows, len(self.lags)), fill_value=np.nan, order='F', dtype=float
        )
        for i, lag in enumerate(self.lags):
            X_data[:, i] = y[self.window_size - lag : -(lag + self.steps - 1)] 

        if X_as_pandas:
            X_data = pd.DataFrame(
                         data    = X_data,
                         columns = self.lags_names,
                         index   = train_index
                     )

    y_data = np.full(
        shape=(n_rows, self.steps), fill_value=np.nan, order='F', dtype=float
    )
    for step in range(self.steps):
        y_data[:, step] = y[self.window_size + step : self.window_size + step + n_rows]

    return X_data, y_data

_create_window_features

_create_window_features(y, train_index, X_as_pandas=False)

Parameters:

Name	Type	Description	Default
y	pandas Series	Training time series.	required
train_index	pandas Index	Index of the training data. It is used to create the pandas DataFrame X_train_window_features when X_as_pandas is True.	required
X_as_pandas	bool	If True, the returned matrix X_train_window_features is a pandas DataFrame.	`False`

Returns:

Name	Type	Description
X_train_window_features	list	List of numpy ndarrays or pandas DataFrames with the window features.
X_train_window_features_names_out_	list	Names of the window features.

Source code in skforecast\direct\_forecaster_direct.py

def _create_window_features(
    self, 
    y: pd.Series,
    train_index: pd.Index,
    X_as_pandas: bool = False,
) -> Tuple[list, list]:
    """

    Parameters
    ----------
    y : pandas Series
        Training time series.
    train_index : pandas Index
        Index of the training data. It is used to create the pandas DataFrame
        `X_train_window_features` when `X_as_pandas` is `True`.
    X_as_pandas : bool, default `False`
        If `True`, the returned matrix `X_train_window_features` is a 
        pandas DataFrame.

    Returns
    -------
    X_train_window_features : list
        List of numpy ndarrays or pandas DataFrames with the window features.
    X_train_window_features_names_out_ : list
        Names of the window features.

    """

    len_train_index = len(train_index)
    X_train_window_features = []
    X_train_window_features_names_out_ = []
    for wf in self.window_features:
        X_train_wf = wf.transform_batch(y)
        if not isinstance(X_train_wf, pd.DataFrame):
            raise TypeError(
                (f"The method `transform_batch` of {type(wf).__name__} "
                 f"must return a pandas DataFrame.")
            )
        X_train_wf = X_train_wf.iloc[-len_train_index:]
        if not len(X_train_wf) == len_train_index:
            raise ValueError(
                (f"The method `transform_batch` of {type(wf).__name__} "
                 f"must return a DataFrame with the same number of rows as "
                 f"the input time series - (`window_size` + (`steps` - 1)): {len_train_index}.")
            )
        X_train_wf.index = train_index

        X_train_window_features_names_out_.extend(X_train_wf.columns)
        if not X_as_pandas:
            X_train_wf = X_train_wf.to_numpy()     
        X_train_window_features.append(X_train_wf)

    return X_train_window_features, X_train_window_features_names_out_

_create_train_X_y

_create_train_X_y(y, exog=None)

Create training matrices from univariate time series and exogenous variables. The resulting matrices contain the target variable and predictors needed to train all the regressors (one per step).

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.	`None`

Returns:

Name	Type	Description
X_train	pandas DataFrame	Training values (predictors) for each step. Note that the index corresponds to that of the last step. It is updated for the corresponding step in the filter_train_X_y_for_step method.
y_train	dict	Values of the time series related to each row of X_train for each step in the form {step: y_step_[i]}.
exog_names_in_	list	Names of the exogenous variables used during training.
X_train_exog_names_out_	list	Names of the exogenous variables included in the matrix X_train created internally for training. It can be different from exog_names_in_ if some exogenous variables are transformed during the training process.
X_train_features_names_out_	list	Names of the columns of the matrix created internally for training.
exog_dtypes_in_	dict	Type of each exogenous variable/s used in training. If transformer_exog is used, the dtypes are calculated before the transformation.

Source code in skforecast\direct\_forecaster_direct.py

def _create_train_X_y(
    self,
    y: pd.Series,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None
) -> Tuple[pd.DataFrame, dict, list, list, list, dict]:
    """
    Create training matrices from univariate time series and exogenous
    variables. The resulting matrices contain the target variable and 
    predictors needed to train all the regressors (one per step).

    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.

    Returns
    -------
    X_train : pandas DataFrame
        Training values (predictors) for each step. Note that the index 
        corresponds to that of the last step. It is updated for the corresponding 
        step in the `filter_train_X_y_for_step` method.
    y_train : dict
        Values of the time series related to each row of `X_train` for each 
        step in the form {step: y_step_[i]}.
    exog_names_in_ : list
        Names of the exogenous variables used during training.
    X_train_exog_names_out_ : list
        Names of the exogenous variables included in the matrix `X_train` created
        internally for training. It can be different from `exog_names_in_` if
        some exogenous variables are transformed during the training process.
    X_train_features_names_out_ : list
        Names of the columns of the matrix created internally for training.
    exog_dtypes_in_ : dict
        Type of each exogenous variable/s used in training. If `transformer_exog` 
        is used, the dtypes are calculated before the transformation.

    """

    check_y(y=y)
    y = input_to_frame(data=y, input_name='y')

    if len(y) < self.window_size + self.steps:
        raise ValueError(
            f"Minimum length of `y` for training this forecaster is "
            f"{self.window_size + self.steps}. Reduce the number of "
            f"predicted steps, {self.steps}, or the maximum "
            f"window_size, {self.window_size}, if no more data is available.\n"
            f"    Length `y`: {len(y)}.\n"
            f"    Max step : {self.steps}.\n"
            f"    Max window size: {self.window_size}.\n"
            f"    Lags window size: {self.max_lag}.\n"
            f"    Window features window size: {self.max_size_window_features}."
        )

    fit_transformer = False if self.is_fitted else True
    y = transform_dataframe(
            df                = y, 
            transformer       = self.transformer_y,
            fit               = fit_transformer,
            inverse_transform = False,
        )
    y_values, y_index = preprocess_y(y=y)

    if self.differentiation is not None:
        if not self.is_fitted:
            y_values = self.differentiator.fit_transform(y_values)
        else:
            differentiator = copy(self.differentiator)
            y_values = differentiator.fit_transform(y_values)

    exog_names_in_ = None
    exog_dtypes_in_ = None
    categorical_features = False
    if exog is not None:
        check_exog(exog=exog, allow_nan=True)
        exog = input_to_frame(data=exog, input_name='exog')

        y_index_no_ws = y_index[self.window_size:]
        len_y = len(y_values)
        len_y_no_ws = len_y - self.window_size
        len_exog = len(exog)
        if not len_exog == len_y and not len_exog == len_y_no_ws:
            raise ValueError(
                f"Length of `exog` must be equal to the length of `y` (if index is "
                f"fully aligned) or length of `y` - `window_size` (if `exog` "
                f"starts after the first `window_size` values).\n"
                f"    `exog`              : ({exog.index[0]} -- {exog.index[-1]})  (n={len_exog})\n"
                f"    `y`                 : ({y.index[0]} -- {y.index[-1]})  (n={len_y})\n"
                f"    `y` - `window_size` : ({y_index_no_ws[0]} -- {y_index_no_ws[-1]})  (n={len_y_no_ws})"
            )

        # NOTE: Need here for filter_train_X_y_for_step to work without fitting
        self.exog_in_ = True
        exog_names_in_ = exog.columns.to_list()
        exog_dtypes_in_ = get_exog_dtypes(exog=exog)

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

        check_exog_dtypes(exog, call_check_exog=True)
        categorical_features = (
            exog.select_dtypes(include=np.number).shape[1] != exog.shape[1]
        )

        _, exog_index = preprocess_exog(exog=exog, return_values=False)
        if len_exog == len_y:
            if not (exog_index == y_index).all():
                raise ValueError(
                    "When `exog` has the same length as `y`, the index of "
                    "`exog` must be aligned with the index of `y` "
                    "to ensure the correct alignment of values."
                )
            # The first `self.window_size` positions have to be removed from 
            # exog since they are not in X_train.
            exog = exog.iloc[self.window_size:, ]
        else:
            if not (exog_index == y_index_no_ws).all():
                raise ValueError(
                    "When `exog` doesn't contain the first `window_size` observations, "
                    "the index of `exog` must be aligned with the index of `y` minus "
                    "the first `window_size` observations to ensure the correct "
                    "alignment of values."
                )

    X_train = []
    X_train_features_names_out_ = []
    train_index = y_index[self.window_size + (self.steps - 1):]
    len_train_index = len(train_index)
    X_as_pandas = True if categorical_features else False

    X_train_lags, y_train = self._create_lags(
        y=y_values, X_as_pandas=X_as_pandas, train_index=train_index
    )
    if X_train_lags is not None:
        X_train.append(X_train_lags)
        X_train_features_names_out_.extend(self.lags_names)

    X_train_window_features_names_out_ = None
    if self.window_features is not None:
        n_diff = 0 if self.differentiation is None else self.differentiation
        end_wf = None if self.steps == 1 else -(self.steps - 1)
        y_window_features = pd.Series(
            y_values[n_diff:end_wf], index=y_index[n_diff:end_wf]
        )
        X_train_window_features, X_train_window_features_names_out_ = (
            self._create_window_features(
                y           = y_window_features, 
                X_as_pandas = X_as_pandas, 
                train_index = train_index
            )
        )
        X_train.extend(X_train_window_features)
        X_train_features_names_out_.extend(X_train_window_features_names_out_)

    # NOTE: Need here for filter_train_X_y_for_step to work without fitting
    self.X_train_window_features_names_out_ = X_train_window_features_names_out_

    X_train_exog_names_out_ = None
    if exog is not None:
        X_train_exog_names_out_ = exog.columns.to_list()
        if X_as_pandas:
            exog_direct, X_train_direct_exog_names_out_ = exog_to_direct(
                exog=exog, steps=self.steps
            )
            exog_direct.index = train_index
        else:
            exog_direct, X_train_direct_exog_names_out_ = exog_to_direct_numpy(
                exog=exog, steps=self.steps
            )

        # NOTE: Need here for filter_train_X_y_for_step to work without fitting
        self.X_train_direct_exog_names_out_ = X_train_direct_exog_names_out_

        X_train_features_names_out_.extend(self.X_train_direct_exog_names_out_)
        X_train.append(exog_direct)

    if len(X_train) == 1:
        X_train = X_train[0]
    else:
        if X_as_pandas:
            X_train = pd.concat(X_train, axis=1)
        else:
            X_train = np.concatenate(X_train, axis=1)

    if X_as_pandas:
        X_train.index = train_index
    else:
        X_train = pd.DataFrame(
                      data    = X_train,
                      index   = train_index,
                      columns = X_train_features_names_out_
                  )

    y_train = {
        step: pd.Series(
                  data  = y_train[:, step - 1], 
                  index = y_index[self.window_size + step - 1:][:len_train_index],
                  name  = f"y_step_{step}"
              )
        for step in range(1, self.steps + 1)
    }

    return (
        X_train,
        y_train,
        exog_names_in_,
        X_train_exog_names_out_,
        X_train_features_names_out_,
        exog_dtypes_in_
    )

create_train_X_y

create_train_X_y(y, exog=None)

Create training matrices from univariate time series and exogenous variables. The resulting matrices contain the target variable and predictors needed to train all the regressors (one per step).

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.	`None`

Returns:

Name	Type	Description
X_train	pandas DataFrame	Training values (predictors) for each step. Note that the index corresponds to that of the last step. It is updated for the corresponding step in the filter_train_X_y_for_step method.
y_train	dict	Values of the time series related to each row of X_train for each step in the form {step: y_step_[i]}.

Source code in skforecast\direct\_forecaster_direct.py

def create_train_X_y(
    self,
    y: pd.Series,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None
) -> Tuple[pd.DataFrame, dict]:
    """
    Create training matrices from univariate time series and exogenous
    variables. The resulting matrices contain the target variable and 
    predictors needed to train all the regressors (one per step).

    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.

    Returns
    -------
    X_train : pandas DataFrame
        Training values (predictors) for each step. Note that the index 
        corresponds to that of the last step. It is updated for the corresponding 
        step in the `filter_train_X_y_for_step` method.
    y_train : dict
        Values of the time series related to each row of `X_train` for each 
        step in the form {step: y_step_[i]}.

    """

    output = self._create_train_X_y(y=y, exog=exog)

    X_train = output[0]
    y_train = output[1]

    return X_train, y_train

filter_train_X_y_for_step

filter_train_X_y_for_step(
    step, X_train, y_train, remove_suffix=False
)

Select the columns needed to train a forecaster for a specific step.
The input matrices should be created using create_train_X_y method. This method updates the index of X_train to the corresponding one according to y_train. If remove_suffix=True the suffix "_step_i" will be removed from the column names.

Parameters:

Name	Type	Description	Default
step	int	Step for which columns must be selected selected. Starts at 1.	required
X_train	pandas DataFrame	Dataframe created with the create_train_X_y method, first return.	required
y_train	dict	Dict created with the create_train_X_y method, second return.	required
remove_suffix	bool	If True, suffix "_step_i" is removed from the column names.	`False`

Returns:

Name	Type	Description
X_train_step	pandas DataFrame	Training values (predictors) for the selected step.
y_train_step	pandas Series	Values of the time series related to each row of X_train.

Source code in skforecast\direct\_forecaster_direct.py

def filter_train_X_y_for_step(
    self,
    step: int,
    X_train: pd.DataFrame,
    y_train: dict,
    remove_suffix: bool = False
) -> Tuple[pd.DataFrame, pd.Series]:
    """
    Select the columns needed to train a forecaster for a specific step.  
    The input matrices should be created using `create_train_X_y` method. 
    This method updates the index of `X_train` to the corresponding one 
    according to `y_train`. If `remove_suffix=True` the suffix "_step_i" 
    will be removed from the column names. 

    Parameters
    ----------
    step : int
        Step for which columns must be selected selected. Starts at 1.
    X_train : pandas DataFrame
        Dataframe created with the `create_train_X_y` method, first return.
    y_train : dict
        Dict created with the `create_train_X_y` method, second return.
    remove_suffix : bool, default `False`
        If True, suffix "_step_i" is removed from the column names.

    Returns
    -------
    X_train_step : pandas DataFrame
        Training values (predictors) for the selected step.
    y_train_step : pandas Series
        Values of the time series related to each row of `X_train`.

    """

    if (step < 1) or (step > self.steps):
        raise ValueError(
            (f"Invalid value `step`. For this forecaster, minimum value is 1 "
             f"and the maximum step is {self.steps}.")
        )

    y_train_step = y_train[step]

    # Matrix X_train starts at index 0.
    if not self.exog_in_:
        X_train_step = X_train
    else:
        n_lags = len(self.lags) if self.lags is not None else 0
        n_window_features = (
            len(self.X_train_window_features_names_out_) if self.window_features is not None else 0
        )
        idx_columns_autoreg = np.arange(n_lags + n_window_features)
        n_exog = len(self.X_train_direct_exog_names_out_) / self.steps
        idx_columns_exog = (
            np.arange((step - 1) * n_exog, (step) * n_exog) + idx_columns_autoreg[-1] + 1
        )
        idx_columns = np.concatenate((idx_columns_autoreg, idx_columns_exog))
        X_train_step = X_train.iloc[:, idx_columns]

    X_train_step.index = y_train_step.index

    if remove_suffix:
        X_train_step.columns = [
            col_name.replace(f"_step_{step}", "")
            for col_name in X_train_step.columns
        ]
        y_train_step.name = y_train_step.name.replace(f"_step_{step}", "")

    return X_train_step, y_train_step

_train_test_split_one_step_ahead

_train_test_split_one_step_ahead(
    y, initial_train_size, exog=None
)

Create matrices needed to train and test the forecaster for one-step-ahead predictions.

Parameters:

Name	Type	Description	Default
y	pandas Series	Training time series.	required
initial_train_size	int	Initial size of the training set. It is the number of observations used to train the forecaster before making the first prediction.	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.	`None`

Returns:

Name	Type	Description
X_train	pandas DataFrame	Predictor values used to train the model.
y_train	dict	Values of the time series related to each row of X_train for each step in the form {step: y_step_[i]}.
X_test	pandas DataFrame	Predictor values used to test the model.
y_test	dict	Values of the time series related to each row of X_test for each step in the form {step: y_step_[i]}.

Source code in skforecast\direct\_forecaster_direct.py

def _train_test_split_one_step_ahead(
    self,
    y: pd.Series,
    initial_train_size: int,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None
) -> Tuple[pd.DataFrame, dict, pd.DataFrame, dict]:
    """
    Create matrices needed to train and test the forecaster for one-step-ahead
    predictions.

    Parameters
    ----------
    y : pandas Series
        Training time series.
    initial_train_size : int
        Initial size of the training set. It is the number of observations used
        to train the forecaster before making the first prediction.
    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.

    Returns
    -------
    X_train : pandas DataFrame
        Predictor values used to train the model.
    y_train : dict
        Values of the time series related to each row of `X_train` for each 
        step in the form {step: y_step_[i]}.
    X_test : pandas DataFrame
        Predictor values used to test the model.
    y_test : dict
        Values of the time series related to each row of `X_test` for each 
        step in the form {step: y_step_[i]}.

    """

    is_fitted = self.is_fitted
    self.is_fitted = False
    X_train, y_train, *_ = self._create_train_X_y(
        y    = y.iloc[: initial_train_size],
        exog = exog.iloc[: initial_train_size] if exog is not None else None
    )

    test_init = initial_train_size - self.window_size
    self.is_fitted = True
    X_test, y_test, *_ = self._create_train_X_y(
        y    = y.iloc[test_init:],
        exog = exog.iloc[test_init:] if exog is not None else None
    )

    self.is_fitted = is_fitted

    return X_train, y_train, X_test, y_test

create_sample_weights

create_sample_weights(X_train)

Crate weights for each observation according to the forecaster's attribute weight_func.

Parameters:

Name	Type	Description	Default
X_train	pandas DataFrame	Dataframe created with create_train_X_y and filter_train_X_y_for_step methods, first return.	required

Returns:

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

Source code in skforecast\direct\_forecaster_direct.py

def create_sample_weights(
    self,
    X_train: pd.DataFrame,
) -> np.ndarray:
    """
    Crate weights for each observation according to the forecaster's attribute
    `weight_func`.

    Parameters
    ----------
    X_train : pandas DataFrame
        Dataframe created with `create_train_X_y` and `filter_train_X_y_for_step`
        methods, first return.

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

    """

    sample_weight = None

    if self.weight_func is not None:
        sample_weight = self.weight_func(X_train.index)

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

    return sample_weight

fit

fit(
    y,
    exog=None,
    store_last_window=True,
    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
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 (last_window_) of training data.	`True`
store_in_sample_residuals	bool	If True, in-sample residuals will be stored in the forecaster object after fitting (in_sample_residuals_ attribute).	`True`

Returns:

Type	Description
None

Source code in skforecast\direct\_forecaster_direct.py

def fit(
    self,
    y: pd.Series,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
    store_last_window: bool = True,
    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
    ----------
    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 (`last_window_`) of training data.
    store_in_sample_residuals : bool, default `True`
        If `True`, in-sample residuals will be stored in the forecaster object
        after fitting (`in_sample_residuals_` attribute).

    Returns
    -------
    None

    """

    # Reset values in case the forecaster has already been fitted.
    self.last_window_                       = None
    self.index_type_                        = None
    self.index_freq_                        = None
    self.training_range_                    = None
    self.exog_in_                           = False
    self.exog_names_in_                     = None
    self.exog_type_in_                      = None
    self.exog_dtypes_in_                    = None
    self.X_train_window_features_names_out_ = None
    self.X_train_exog_names_out_            = None
    self.X_train_direct_exog_names_out_     = None
    self.X_train_features_names_out_        = None
    self.in_sample_residuals_               = {step: None for step in range(1, self.steps + 1)}
    self.is_fitted                          = False
    self.fit_date                           = None

    (
        X_train,
        y_train,
        exog_names_in_,
        X_train_exog_names_out_,
        X_train_features_names_out_,
        exog_dtypes_in_
    ) = self._create_train_X_y(y=y, exog=exog)

    def fit_forecaster(regressor, X_train, y_train, step, store_in_sample_residuals):
        """
        Auxiliary function to fit each of the forecaster's regressors in parallel.

        Parameters
        ----------
        regressor : object
            Regressor to be fitted.
        X_train : pandas DataFrame
            Dataframe created with the `create_train_X_y` method, first return.
        y_train : dict
            Dict created with the `create_train_X_y` method, second return.
        step : int
            Step of the forecaster to be fitted.
        store_in_sample_residuals : bool
            If `True`, in-sample residuals will be stored in the forecaster object
            after fitting (`in_sample_residuals_` attribute).

        Returns
        -------
        Tuple with the step, fitted regressor and in-sample residuals.

        """

        X_train_step, y_train_step = self.filter_train_X_y_for_step(
                                         step          = step,
                                         X_train       = X_train,
                                         y_train       = y_train,
                                         remove_suffix = True
                                     )
        sample_weight = self.create_sample_weights(X_train=X_train_step)
        if sample_weight is not None:
            regressor.fit(
                X             = X_train_step,
                y             = y_train_step,
                sample_weight = sample_weight,
                **self.fit_kwargs
            )
        else:
            regressor.fit(
                X = X_train_step,
                y = y_train_step,
                **self.fit_kwargs
            )

        # This is done to save time during fit in functions such as backtesting()
        if store_in_sample_residuals:
            residuals = (
                (y_train_step - regressor.predict(X_train_step))
            ).to_numpy()

            if len(residuals) > 1000:
                rng = np.random.default_rng(seed=123)
                residuals = rng.choice(
                                a       = residuals, 
                                size    = 1000, 
                                replace = False
                            )
        else:
            residuals = None

        return step, regressor, residuals

    results_fit = (
        Parallel(n_jobs=self.n_jobs)
        (delayed(fit_forecaster)
        (
            regressor                 = copy(self.regressor),
            X_train                   = X_train,
            y_train                   = y_train,
            step                      = step,
            store_in_sample_residuals = store_in_sample_residuals
        )
        for step in range(1, self.steps + 1))
    )

    self.regressors_ = {step: regressor for step, regressor, _ in results_fit}

    if store_in_sample_residuals:
        self.in_sample_residuals_ = {
            step: residuals 
            for step, _, residuals in results_fit
        }

    self.X_train_features_names_out_ = X_train_features_names_out_

    self.is_fitted = True
    self.fit_date = pd.Timestamp.today().strftime('%Y-%m-%d %H:%M:%S')
    self.training_range_ = preprocess_y(y=y, return_values=False)[1][[0, -1]]
    self.index_type_ = type(X_train.index)
    if isinstance(X_train.index, pd.DatetimeIndex):
        self.index_freq_ = X_train.index.freqstr
    else: 
        self.index_freq_ = X_train.index.step

    if exog is not None:
        self.exog_in_ = True
        self.exog_type_in_ = type(exog)
        self.exog_names_in_ = exog_names_in_
        self.exog_dtypes_in_ = exog_dtypes_in_
        self.X_train_exog_names_out_ = X_train_exog_names_out_

    if store_last_window:
        self.last_window_ = (
            y.iloc[-self.window_size:]
            .copy()
            .to_frame(name=y.name if y.name is not None else 'y')
        )

_create_predict_inputs

_create_predict_inputs(
    steps=None,
    last_window=None,
    exog=None,
    check_inputs=True,
)

Create the inputs needed for the prediction process.

Parameters:

Name	Type	Description	Default
steps	(int, list, None)	Predict n steps. The value of steps must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1. If int: Only steps within the range of 1 to int are predicted. If list: List of ints. Only the steps contained in the list are predicted. If None: As many steps are predicted as were defined at initialization.	`None`
last_window	pandas Series, pandas DataFrame	Series values used to create the predictors (lags) needed to predict steps. 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`
check_inputs	bool	If True, the input is checked for possible warnings and errors with the check_predict_input function. This argument is created for internal use and is not recommended to be changed.	`True`

Returns:

Name	Type	Description
Xs	list	List of numpy arrays with the predictors for each step.
Xs_col_names	list	Names of the columns of the matrix created internally for prediction.
steps	list	Steps to predict.
prediction_index	pandas Index	Index of the predictions.

Source code in skforecast\direct\_forecaster_direct.py

def _create_predict_inputs(
    self,
    steps: Optional[Union[int, list]] = None,
    last_window: Optional[Union[pd.Series, pd.DataFrame]] = None,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
    check_inputs: bool = True
) -> Tuple[list, list, list, pd.Index]:
    """
    Create the inputs needed for the prediction process.

    Parameters
    ----------
    steps : int, list, None, default `None`
        Predict n steps. The value of `steps` must be less than or equal to the 
        value of steps defined when initializing the forecaster. Starts at 1.

        - If `int`: Only steps within the range of 1 to int are predicted.
        - If `list`: List of ints. Only the steps contained in the list 
        are predicted.
        - If `None`: As many steps are predicted as were defined at 
        initialization.
    last_window : pandas Series, pandas DataFrame, default `None`
        Series values used to create the predictors (lags) needed to 
        predict `steps`.
        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.
    check_inputs : bool, default `True`
        If `True`, the input is checked for possible warnings and errors 
        with the `check_predict_input` function. This argument is created 
        for internal use and is not recommended to be changed.

    Returns
    -------
    Xs : list
        List of numpy arrays with the predictors for each step.
    Xs_col_names : list
        Names of the columns of the matrix created internally for prediction.
    steps : list
        Steps to predict.
    prediction_index : pandas Index
        Index of the predictions.

    """

    steps = prepare_steps_direct(
                max_step = self.steps,
                steps    = steps
            )

    if last_window is None:
        last_window = self.last_window_

    if check_inputs:
        check_predict_input(
            forecaster_name = type(self).__name__,
            steps           = steps,
            is_fitted       = self.is_fitted,
            exog_in_        = self.exog_in_,
            index_type_     = self.index_type_,
            index_freq_     = self.index_freq_,
            window_size     = self.window_size,
            last_window     = last_window,
            exog            = exog,
            exog_type_in_   = self.exog_type_in_,
            exog_names_in_  = self.exog_names_in_,
            interval        = None,
            max_steps       = self.steps
        )

    last_window = last_window.iloc[-self.window_size:].copy()
    last_window_values, last_window_index = preprocess_last_window(
                                                last_window = last_window
                                            )

    last_window_values = transform_numpy(
                             array             = last_window_values,
                             transformer       = self.transformer_y,
                             fit               = False,
                             inverse_transform = False
                         )
    if self.differentiation is not None:
        last_window_values = self.differentiator.fit_transform(last_window_values)

    X_autoreg = []
    Xs_col_names = []
    if self.lags is not None:
        X_lags = last_window_values[-self.lags]
        X_autoreg.append(X_lags)
        Xs_col_names.extend(self.lags_names)

    if self.window_features is not None:
        n_diff = 0 if self.differentiation is None else self.differentiation
        X_window_features = np.concatenate(
            [
                wf.transform(last_window_values[n_diff:])
                for wf in self.window_features
            ]
        )
        X_autoreg.append(X_window_features)
        Xs_col_names.extend(self.X_train_window_features_names_out_)

    X_autoreg = np.concatenate(X_autoreg).reshape(1, -1)
    if exog is not None:
        exog = input_to_frame(data=exog, input_name='exog')
        exog = exog.loc[:, self.exog_names_in_]
        exog = transform_dataframe(
                   df                = exog,
                   transformer       = self.transformer_exog,
                   fit               = False,
                   inverse_transform = False
               )
        check_exog_dtypes(exog=exog)
        exog_values, _ = exog_to_direct_numpy(
                             exog  = exog.to_numpy()[:max(steps)],
                             steps = max(steps)
                         )
        exog_values = exog_values[0]

        n_exog = exog.shape[1]
        Xs = [
            np.concatenate(
                [
                    X_autoreg,
                    exog_values[(step - 1) * n_exog : step * n_exog].reshape(1, -1),
                ],
                axis=1
            )
            for step in steps
        ]
        # HACK: This is not the best way to do it. Can have any problem
        # if the exog_columns are not in the same order as the
        # self.window_features_names.
        Xs_col_names = Xs_col_names + exog.columns.to_list()
    else:
        Xs = [X_autoreg] * len(steps)

    prediction_index = expand_index(
                           index = last_window_index,
                           steps = max(steps)
                       )[np.array(steps) - 1]
    if isinstance(last_window_index, pd.DatetimeIndex) and np.array_equal(
        steps, np.arange(min(steps), max(steps) + 1)
    ):
        prediction_index.freq = last_window_index.freq

    # HACK: Why no use self.X_train_features_names_out_ as Xs_col_names?
    return Xs, Xs_col_names, steps, prediction_index

create_predict_X

create_predict_X(steps=None, last_window=None, exog=None)

Create the predictors needed to predict steps ahead.

Parameters:

Name	Type	Description	Default
steps	(int, list, None)	Predict n steps. The value of steps must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1. If int: Only steps within the range of 1 to int are predicted. If list: List of ints. Only the steps contained in the list are predicted. If None: As many steps are predicted as were defined at initialization.	`None`
last_window	pandas Series, pandas DataFrame	Series values used to create the predictors (lags) needed to predict steps. 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
X_predict	pandas DataFrame	Pandas DataFrame with the predictors for each step. The index is the same as the prediction index.

Source code in skforecast\direct\_forecaster_direct.py

def create_predict_X(
    self,
    steps: Optional[Union[int, list]] = None,
    last_window: Optional[Union[pd.Series, pd.DataFrame]] = None,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None
) -> pd.DataFrame:
    """
    Create the predictors needed to predict `steps` ahead.

    Parameters
    ----------
    steps : int, list, None, default `None`
        Predict n steps. The value of `steps` must be less than or equal to the 
        value of steps defined when initializing the forecaster. Starts at 1.

        - If `int`: Only steps within the range of 1 to int are predicted.
        - If `list`: List of ints. Only the steps contained in the list 
        are predicted.
        - If `None`: As many steps are predicted as were defined at 
        initialization.
    last_window : pandas Series, pandas DataFrame, default `None`
        Series values used to create the predictors (lags) needed to 
        predict `steps`.
        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
    -------
    X_predict : pandas DataFrame
        Pandas DataFrame with the predictors for each step. The index 
        is the same as the prediction index.

    """

    Xs, Xs_col_names, steps, prediction_index = self._create_predict_inputs(
        steps=steps, last_window=last_window, exog=exog
    )

    X_predict = pd.DataFrame(
                    data    = np.concatenate(Xs, axis=0), 
                    columns = Xs_col_names, 
                    index   = prediction_index
                )

    if self.transformer_y is not None or self.differentiation is not None:
        warnings.warn(
            "The output matrix is in the transformed scale due to the "
            "inclusion of transformations or differentiation in the Forecaster. "
            "As a result, any predictions generated using this matrix will also "
            "be in the transformed scale. Please refer to the documentation "
            "for more details: "
            "https://skforecast.org/latest/user_guides/training-and-prediction-matrices.html",
            DataTransformationWarning
        )

    return X_predict

predict

predict(
    steps=None,
    last_window=None,
    exog=None,
    check_inputs=True,
)

Predict n steps ahead.

Parameters:

Name	Type	Description	Default
steps	(int, list, None)	Predict n steps. The value of steps must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1. If int: Only steps within the range of 1 to int are predicted. If list: List of ints. Only the steps contained in the list are predicted. If None: As many steps are predicted as were defined at initialization.	`None`
last_window	pandas Series, pandas DataFrame	Series values used to create the predictors (lags) needed to predict steps. 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`
check_inputs	bool	If True, the input is checked for possible warnings and errors with the check_predict_input function. This argument is created for internal use and is not recommended to be changed.	`True`

Returns:

Name	Type	Description
predictions	pandas Series	Predicted values.

Source code in skforecast\direct\_forecaster_direct.py

def predict(
    self,
    steps: Optional[Union[int, list]] = None,
    last_window: Optional[Union[pd.Series, pd.DataFrame]] = None,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
    check_inputs: bool = True
) -> pd.Series:
    """
    Predict n steps ahead.

    Parameters
    ----------
    steps : int, list, None, default `None`
        Predict n steps. The value of `steps` must be less than or equal to the 
        value of steps defined when initializing the forecaster. Starts at 1.

        - If `int`: Only steps within the range of 1 to int are predicted.
        - If `list`: List of ints. Only the steps contained in the list 
        are predicted.
        - If `None`: As many steps are predicted as were defined at 
        initialization.
    last_window : pandas Series, pandas DataFrame, default `None`
        Series values used to create the predictors (lags) needed to 
        predict `steps`.
        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.
    check_inputs : bool, default `True`
        If `True`, the input is checked for possible warnings and errors 
        with the `check_predict_input` function. This argument is created 
        for internal use and is not recommended to be changed.

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

    """

    Xs, _, steps, prediction_index = self._create_predict_inputs(
        steps=steps, last_window=last_window, exog=exog, check_inputs=check_inputs
    )

    regressors = [self.regressors_[step] for step in steps]
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", 
            message="X does not have valid feature names", 
            category=UserWarning
        )
        predictions = np.array([
            regressor.predict(X).ravel()[0] 
            for regressor, X in zip(regressors, Xs)
        ])

    if self.differentiation is not None:
        predictions = self.differentiator.inverse_transform_next_window(predictions)

    predictions = transform_numpy(
                      array             = predictions,
                      transformer       = self.transformer_y,
                      fit               = False,
                      inverse_transform = True
                  )

    predictions = pd.Series(
                      data  = predictions,
                      index = prediction_index,
                      name  = 'pred'
                  )

    return predictions

predict_bootstrapping

predict_bootstrapping(
    steps=None,
    last_window=None,
    exog=None,
    n_boot=250,
    random_state=123,
    use_in_sample_residuals=True,
    use_binned_residuals=None,
)

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, list, None)	Predict n steps. The value of steps must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1. If int: Only steps within the range of 1 to int are predicted. If list: List of ints. Only the steps contained in the list are predicted. If None: As many steps are predicted as were defined at initialization.	`None`
last_window	pandas Series	Series values used to create the predictors (lags) needed to predict steps. 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 predictions.	`250`
random_state	int	Sets a seed to the random generator, so that boot predictions are always deterministic.	`123`
use_in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create predictions. 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`
use_binned_residuals	Ignored	Not used, present here for API consistency by convention.	None

Returns:

Name	Type	Description
boot_predictions	pandas DataFrame	Predictions generated by bootstrapping. 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\direct\_forecaster_direct.py

def predict_bootstrapping(
    self,
    steps: Optional[Union[int, list]] = None,
    last_window: Optional[pd.Series] = None,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
    n_boot: int = 250,
    random_state: int = 123,
    use_in_sample_residuals: bool = True,
    use_binned_residuals: Any = None
) -> pd.DataFrame:
    """
    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, list, None, default `None`
        Predict n steps. The value of `steps` must be less than or equal to the 
        value of steps defined when initializing the forecaster. Starts at 1.

        - If `int`: Only steps within the range of 1 to int are predicted.
        - If `list`: List of ints. Only the steps contained in the list 
        are predicted.
        - If `None`: As many steps are predicted as were defined at 
        initialization.
    last_window : pandas Series, default `None`
        Series values used to create the predictors (lags) needed to 
        predict `steps`.
        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 `250`
        Number of bootstrapping iterations used to estimate predictions.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot predictions are always 
        deterministic.
    use_in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of
        prediction error to create predictions. 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()`).
    use_binned_residuals : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    boot_predictions : pandas DataFrame
        Predictions generated by bootstrapping.
        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 self.is_fitted:

        steps = prepare_steps_direct(
                    steps    = steps,
                    max_step = self.steps
                )

        if use_in_sample_residuals:
            if not set(steps).issubset(set(self.in_sample_residuals_.keys())):
                raise ValueError(
                    f"Not `forecaster.in_sample_residuals_` for steps: "
                    f"{set(steps) - set(self.in_sample_residuals_.keys())}."
                )
            residuals = self.in_sample_residuals_
        else:
            if self.out_sample_residuals_ is None:
                raise ValueError(
                    "`forecaster.out_sample_residuals_` is `None`. Use "
                    "`use_in_sample_residuals=True` or the "
                    "`set_out_sample_residuals()` method before predicting."
                )
            else:
                if not set(steps).issubset(set(self.out_sample_residuals_.keys())):
                    raise ValueError(
                        f"No `forecaster.out_sample_residuals_` for steps: "
                        f"{set(steps) - set(self.out_sample_residuals_.keys())}. "
                        f"Use method `set_out_sample_residuals()`."
                    )
            residuals = self.out_sample_residuals_

        check_residuals = (
            "forecaster.in_sample_residuals_" if use_in_sample_residuals
            else "forecaster.out_sample_residuals_"
        )
        for step in steps:
            if residuals[step] is None:
                raise ValueError(
                    f"forecaster residuals for step {step} are `None`. "
                    f"Check {check_residuals}."
                )
            elif any(x is None or np.isnan(x) for x in residuals[step]):
                raise ValueError(
                    f"forecaster residuals for step {step} contains `None` values. "
                    f"Check {check_residuals}."
                )

    Xs, _, steps, prediction_index = self._create_predict_inputs(
        steps=steps, last_window=last_window, exog=exog
    )

    # NOTE: Predictions must be transformed and differenced before adding residuals
    regressors = [self.regressors_[step] for step in steps]
    with warnings.catch_warnings():
        warnings.filterwarnings(
            "ignore", 
            message="X does not have valid feature names", 
            category=UserWarning
        )
        predictions = np.array([
            regressor.predict(X).ravel()[0] 
            for regressor, X in zip(regressors, Xs)
        ])

    boot_predictions = np.tile(predictions, (n_boot, 1)).T
    boot_columns = [f"pred_boot_{i}" for i in range(n_boot)]

    rng = np.random.default_rng(seed=random_state)
    for i, step in enumerate(steps):
        sampled_residuals = residuals[step][
            rng.integers(low=0, high=len(residuals[step]), size=n_boot)
        ]
        boot_predictions[i, :] = boot_predictions[i, :] + sampled_residuals

    if self.differentiation is not None:
        boot_predictions = (
            self.differentiator.inverse_transform_next_window(boot_predictions)
        )

    if self.transformer_y:
        boot_predictions = np.apply_along_axis(
                               func1d            = transform_numpy,
                               axis              = 0,
                               arr               = boot_predictions,
                               transformer       = self.transformer_y,
                               fit               = False,
                               inverse_transform = True
                           )

    boot_predictions = pd.DataFrame(
                           data    = boot_predictions,
                           index   = prediction_index,
                           columns = boot_columns
                       )

    return boot_predictions

predict_interval

predict_interval(
    steps=None,
    last_window=None,
    exog=None,
    interval=[5, 95],
    n_boot=250,
    random_state=123,
    use_in_sample_residuals=True,
    use_binned_residuals=None,
)

Bootstrapping based predicted intervals. Both predictions and intervals are returned.

Parameters:

Name	Type	Description	Default
steps	(int, list, None)	Predict n steps. The value of steps must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1. If int: Only steps within the range of 1 to int are predicted. If list: List of ints. Only the steps contained in the list are predicted. If None: As many steps are predicted as were defined at initialization.	`None`
last_window	pandas Series	Series values used to create the predictors (lags) needed to predict steps. 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 predictions.	`250`
random_state	int	Sets a seed to the random generator, so that boot predictions are always deterministic.	`123`
use_in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create predictions. 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`
use_binned_residuals	Ignored	Not used, present here for API consistency by convention.	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.

Notes

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

Source code in skforecast\direct\_forecaster_direct.py

def predict_interval(
    self,
    steps: Optional[Union[int, list]] = None,
    last_window: Optional[pd.Series] = None,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
    interval: list = [5, 95],
    n_boot: int = 250,
    random_state: int = 123,
    use_in_sample_residuals: bool = True,
    use_binned_residuals: Any = None
) -> pd.DataFrame:
    """
    Bootstrapping based predicted intervals.
    Both predictions and intervals are returned.

    Parameters
    ----------
    steps : int, list, None, default `None`
        Predict n steps. The value of `steps` must be less than or equal to the 
        value of steps defined when initializing the forecaster. Starts at 1.

        - If `int`: Only steps within the range of 1 to int are predicted.
        - If `list`: List of ints. Only the steps contained in the list 
        are predicted.
        - If `None`: As many steps are predicted as were defined at 
        initialization.
    last_window : pandas Series, default `None`
        Series values used to create the predictors (lags) needed to 
        predict `steps`.
        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 `250`
        Number of bootstrapping iterations used to estimate predictions.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot predictions are always 
        deterministic.
    use_in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of
        prediction error to create predictions. 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()`).
    use_binned_residuals : Ignored
        Not used, present here for API consistency by convention.

    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.

    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.

    """

    check_interval(interval=interval)

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

    predictions = self.predict(
                      steps        = steps,
                      last_window  = last_window,
                      exog         = exog,
                      check_inputs = False
                  )

    interval = np.array(interval) / 100
    predictions_interval = boot_predictions.quantile(q=interval, axis=1).transpose()
    predictions_interval.columns = ['lower_bound', 'upper_bound']
    predictions = pd.concat((predictions, predictions_interval), axis=1)

    return predictions

predict_quantiles

predict_quantiles(
    steps=None,
    last_window=None,
    exog=None,
    quantiles=[0.05, 0.5, 0.95],
    n_boot=250,
    random_state=123,
    use_in_sample_residuals=True,
    use_binned_residuals=None,
)

Bootstrapping based predicted quantiles.

Parameters:

Name	Type	Description	Default
steps	(int, list, None)	Predict n steps. The value of steps must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1. If int: Only steps within the range of 1 to int are predicted. If list: List of ints. Only the steps contained in the list are predicted. If None: As many steps are predicted as were defined at initialization.	`None`
last_window	pandas Series	Series values used to create the predictors (lags) needed to predict steps. 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`
quantiles	list	Sequence of quantiles to compute, which must be between 0 and 1 inclusive. For example, quantiles of 0.05, 0.5 and 0.95 should be as quantiles = [0.05, 0.5, 0.95].	`[0.05, 0.5, 0.95]`
n_boot	int	Number of bootstrapping iterations used to estimate quantiles.	`250`
random_state	int	Sets a seed to the random generator, so that boot quantiles are always deterministic.	`123`
use_in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create prediction quantiles. 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`
use_binned_residuals	Ignored	Not used, present here for API consistency by convention.	None

Returns:

Name	Type	Description
predictions	pandas DataFrame	Quantiles predicted by the forecaster.

Notes

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

Source code in skforecast\direct\_forecaster_direct.py

def predict_quantiles(
    self,
    steps: Optional[Union[int, list]] = None,
    last_window: Optional[pd.Series] = None,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
    quantiles: list = [0.05, 0.5, 0.95],
    n_boot: int = 250,
    random_state: int = 123,
    use_in_sample_residuals: bool = True,
    use_binned_residuals: Any = None
) -> pd.DataFrame:
    """
    Bootstrapping based predicted quantiles.

    Parameters
    ----------
    steps : int, list, None, default `None`
        Predict n steps. The value of `steps` must be less than or equal to the 
        value of steps defined when initializing the forecaster. Starts at 1.

        - If `int`: Only steps within the range of 1 to int are predicted.
        - If `list`: List of ints. Only the steps contained in the list 
        are predicted.
        - If `None`: As many steps are predicted as were defined at 
        initialization.
    last_window : pandas Series, default `None`
        Series values used to create the predictors (lags) needed to 
        predict `steps`.
        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.
    quantiles : list, default `[0.05, 0.5, 0.95]`
        Sequence of quantiles to compute, which must be between 0 and 1 
        inclusive. For example, quantiles of 0.05, 0.5 and 0.95 should be as 
        `quantiles = [0.05, 0.5, 0.95]`.
    n_boot : int, default `250`
        Number of bootstrapping iterations used to estimate quantiles.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot quantiles are always 
        deterministic.
    use_in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of
        prediction error to create prediction quantiles. 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()`).
    use_binned_residuals : Ignored
        Not used, present here for API consistency by convention.

    Returns
    -------
    predictions : pandas DataFrame
        Quantiles predicted by the forecaster.

    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.

    """

    check_interval(quantiles=quantiles)

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

    predictions = boot_predictions.quantile(q=quantiles, axis=1).transpose()
    predictions.columns = [f'q_{q}' for q in quantiles]

    return predictions

predict_dist

predict_dist(
    distribution,
    steps=None,
    last_window=None,
    exog=None,
    n_boot=250,
    random_state=123,
    use_in_sample_residuals=True,
    use_binned_residuals=None,
)

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
distribution	Object	A distribution object from scipy.stats.	required
steps	(int, list, None)	Predict n steps. The value of steps must be less than or equal to the value of steps defined when initializing the forecaster. Starts at 1. If int: Only steps within the range of 1 to int are predicted. If list: List of ints. Only the steps contained in the list are predicted. If None: As many steps are predicted as were defined at initialization.	`None`
last_window	pandas Series	Series values used to create the predictors (lags) needed to predict steps. 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 predictions.	`250`
random_state	int	Sets a seed to the random generator, so that boot predictions are always deterministic.	`123`
use_in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create predictions. 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`
use_binned_residuals	Ignored	Not used, present here for API consistency by convention.	None

Returns:

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

Source code in skforecast\direct\_forecaster_direct.py

def predict_dist(
    self,
    distribution: object,
    steps: Optional[Union[int, list]] = None,
    last_window: Optional[pd.Series] = None,
    exog: Optional[Union[pd.Series, pd.DataFrame]] = None,
    n_boot: int = 250,
    random_state: int = 123,
    use_in_sample_residuals: bool = True,
    use_binned_residuals: Any = None
) -> 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
    ----------
    distribution : Object
        A distribution object from scipy.stats.
    steps : int, list, None, default `None`
        Predict n steps. The value of `steps` must be less than or equal to the 
        value of steps defined when initializing the forecaster. Starts at 1.

        - If `int`: Only steps within the range of 1 to int are predicted.
        - If `list`: List of ints. Only the steps contained in the list 
        are predicted.
        - If `None`: As many steps are predicted as were defined at 
        initialization.
    last_window : pandas Series, default `None`
        Series values used to create the predictors (lags) needed to 
        predict `steps`.
        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 `250`
        Number of bootstrapping iterations used to estimate predictions.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot predictions are always 
        deterministic.
    use_in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of
        prediction error to create predictions. 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()`).
    use_binned_residuals : Ignored
        Not used, present here for API consistency by convention.

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

    """

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

    param_names = [p for p in inspect.signature(distribution._pdf).parameters
                   if not p == 'x'] + ["loc", "scale"]
    param_values = np.apply_along_axis(
                       lambda x: distribution.fit(x),
                       axis = 1,
                       arr  = boot_samples
                   )
    predictions = pd.DataFrame(
                      data    = param_values,
                      columns = param_names,
                      index   = boot_samples.index
                  )

    return predictions

set_params

set_params(params)

Set new values to the parameters of the scikit learn model stored in the forecaster. It is important to note that all models share the same configuration of parameters and hyperparameters.

Parameters:

Name	Type	Description	Default
params	dict	Parameters values.	required

Returns:

Type	Description
None

Source code in skforecast\direct\_forecaster_direct.py

def set_params(
    self, 
    params: dict
) -> None:
    """
    Set new values to the parameters of the scikit learn model stored in the
    forecaster. It is important to note that all models share the same 
    configuration of parameters and hyperparameters.

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

    Returns
    -------
    None

    """

    self.regressor = clone(self.regressor)
    self.regressor.set_params(**params)
    self.regressors_ = {
        step: clone(self.regressor)
        for step in range(1, self.steps + 1)
    }

set_fit_kwargs

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\direct\_forecaster_direct.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_lags

set_lags(lags=None)

Set new value to the attribute lags. Attributes lags_names, max_lag and window_size are also updated.

Parameters:

Name	Type	Description	Default
lags	int, list, numpy ndarray, range	Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1. int: include lags from 1 to lags (included). list, 1d numpy ndarray or range: include only lags present in lags, all elements must be int. None: no lags are included as predictors.	`None`

Returns:

Type	Description
None

Source code in skforecast\direct\_forecaster_direct.py

def set_lags(
    self, 
    lags: Optional[Union[int, list, np.ndarray, range]] = None
) -> None:
    """
    Set new value to the attribute `lags`. Attributes `lags_names`, 
    `max_lag` and `window_size` are also updated.

    Parameters
    ----------
    lags : int, list, numpy ndarray, range, default `None`
        Lags used as predictors. Index starts at 1, so lag 1 is equal to t-1. 

        - `int`: include lags from 1 to `lags` (included).
        - `list`, `1d numpy ndarray` or `range`: include only lags present in 
        `lags`, all elements must be int.
        - `None`: no lags are included as predictors. 

    Returns
    -------
    None

    """

    if self.window_features is None and lags is None:
        raise ValueError(
            ("At least one of the arguments `lags` or `window_features` "
             "must be different from None. This is required to create the "
             "predictors used in training the forecaster.")
        )

    self.lags, self.lags_names, self.max_lag = initialize_lags(type(self).__name__, lags)
    self.window_size = max(
        [ws for ws in [self.max_lag, self.max_size_window_features] 
         if ws is not None]
    )
    if self.differentiation is not None:
        self.window_size += self.differentiation
        self.differentiator.set_params(window_size=self.window_size)

set_window_features

set_window_features(window_features=None)

Set new value to the attribute window_features. Attributes max_size_window_features, window_features_names, window_features_class_names and window_size are also updated.

Parameters:

Name	Type	Description	Default
window_features	(object, list)	Instance or list of instances used to create window features. Window features are created from the original time series and are included as predictors.	`None`

Returns:

Type	Description
None

Source code in skforecast\direct\_forecaster_direct.py

def set_window_features(
    self, 
    window_features: Optional[Union[object, list]] = None
) -> None:
    """
    Set new value to the attribute `window_features`. Attributes 
    `max_size_window_features`, `window_features_names`, 
    `window_features_class_names` and `window_size` are also updated.

    Parameters
    ----------
    window_features : object, list, default `None`
        Instance or list of instances used to create window features. Window features
        are created from the original time series and are included as predictors.

    Returns
    -------
    None

    """

    if window_features is None and self.lags is None:
        raise ValueError(
            ("At least one of the arguments `lags` or `window_features` "
             "must be different from None. This is required to create the "
             "predictors used in training the forecaster.")
        )

    self.window_features, self.window_features_names, self.max_size_window_features = (
        initialize_window_features(window_features)
    )
    self.window_features_class_names = None
    if window_features is not None:
        self.window_features_class_names = [
            type(wf).__name__ for wf in self.window_features
        ] 
    self.window_size = max(
        [ws for ws in [self.max_lag, self.max_size_window_features] 
         if ws is not None]
    )
    if self.differentiation is not None:
        self.window_size += self.differentiation   
        self.differentiator.set_params(window_size=self.window_size)

set_out_sample_residuals

set_out_sample_residuals(
    y_true, y_pred, append=False, random_state=36987
)

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. y_true and y_pred are expected to be in the original scale of the time series. Residuals are calculated as y_true - y_pred, after applying the necessary transformations and differentiations if the forecaster includes them (self.transformer_y and self.differentiation).

A total of 10_000 residuals are stored in the attribute out_sample_residuals_. If the number of residuals is greater than 10_000, a random sample of 10_000 residuals is stored.

Parameters:

Name	Type	Description	Default
y_true	dict	Dictionary of numpy ndarrays or pandas Series with the true values of the time series for each model in the form {step: y_true}.	required
y_pred	dict	Dictionary of numpy ndarrays or pandas Series with the predicted values of the time series for each model in the form {step: y_pred}.	required
append	bool	If True, new residuals are added to the once already stored in the attribute out_sample_residuals_. If after appending the new residuals, the limit of 10_000 samples is exceeded, a random sample of 10_000 is kept.	`False`
random_state	int	Sets a seed to the random sampling for reproducible output.	`36987`

Returns:

Type	Description
None

Source code in skforecast\direct\_forecaster_direct.py

def set_out_sample_residuals(
    self,
    y_true: dict,
    y_pred: dict,
    append: bool = False,
    random_state: int = 36987
) -> 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. `y_true` and `y_pred` are expected
    to be in the original scale of the time series. Residuals are calculated
    as `y_true` - `y_pred`, after applying the necessary transformations and
    differentiations if the forecaster includes them (`self.transformer_y`
    and `self.differentiation`).

    A total of 10_000 residuals are stored in the attribute `out_sample_residuals_`.
    If the number of residuals is greater than 10_000, a random sample of
    10_000 residuals is stored.

    Parameters
    ----------
    y_true : dict
        Dictionary of numpy ndarrays or pandas Series with the true values of
        the time series for each model in the form {step: y_true}.
    y_pred : dict
        Dictionary of numpy ndarrays or pandas Series with the predicted values
        of the time series for each model in the form {step: y_pred}.
    append : bool, default `False`
        If `True`, new residuals are added to the once already stored in the
        attribute `out_sample_residuals_`. If after appending the new residuals,
        the limit of 10_000 samples is exceeded, a random sample of 10_000 is
        kept.
    random_state : int, default `36987`
        Sets a seed to the random sampling for reproducible output.

    Returns
    -------
    None

    """

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

    if not isinstance(y_true, dict):
        raise TypeError(
            f"`y_true` must be a dictionary of numpy ndarrays or pandas Series. "
            f"Got {type(y_true)}."
        )

    if not isinstance(y_pred, dict):
        raise TypeError(
            f"`y_pred` must be a dictionary of numpy ndarrays or pandas Series. "
            f"Got {type(y_pred)}."
        )

    if not set(y_true.keys()) == set(y_pred.keys()):
        raise ValueError(
            f"`y_true` and `y_pred` must have the same keys. "
            f"Got {set(y_true.keys())} and {set(y_pred.keys())}."
        )

    for k in y_true.keys():
        if not isinstance(y_true[k], (np.ndarray, pd.Series)):
            raise TypeError(
                f"Values of `y_true` must be numpy ndarrays or pandas Series. "
                f"Got {type(y_true[k])} for step {k}."
            )
        if not isinstance(y_pred[k], (np.ndarray, pd.Series)):
            raise TypeError(
                f"Values of `y_pred` must be numpy ndarrays or pandas Series. "
                f"Got {type(y_pred[k])} for step {k}."
            )
        if len(y_true[k]) != len(y_pred[k]):
            raise ValueError(
                f"`y_true` and `y_pred` must have the same length. "
                f"Got {len(y_true[k])} and {len(y_pred[k])} for step {k}."
            )
        if isinstance(y_true[k], pd.Series) and isinstance(y_pred[k], pd.Series):
            if not y_true[k].index.equals(y_pred[k].index):
                raise ValueError(
                    f"When containing pandas Series, elements in `y_true` and "
                    f"`y_pred` must have the same index. Error in step {k}."
                )

    if self.out_sample_residuals_ is None:
        self.out_sample_residuals_ = {
            step: None for step in range(1, self.steps + 1)
        }

    steps_to_update = set(range(1, self.steps + 1)).intersection(set(y_pred.keys()))
    if not steps_to_update:
        raise ValueError(
            "Provided keys in `y_pred` and `y_true` do not match any step. "
            "Residuals cannot be updated."
        )

    residuals = {}
    rng = np.random.default_rng(seed=random_state)
    y_true = y_true.copy()
    y_pred = y_pred.copy()
    if self.differentiation is not None:
        differentiator = copy(self.differentiator)
        differentiator.set_params(window_size=None)

    for k in steps_to_update:
        if isinstance(y_true[k], pd.Series):
            y_true[k] = y_true[k].to_numpy()
        if isinstance(y_pred[k], pd.Series):
            y_pred[k] = y_pred[k].to_numpy()
        if self.transformer_y:
            y_true[k] = transform_numpy(
                            array             = y_true[k],
                            transformer       = self.transformer_y,
                            fit               = False,
                            inverse_transform = False
                        )
            y_pred[k] = transform_numpy(
                            array             = y_pred[k],
                            transformer       = self.transformer_y,
                            fit               = False,
                            inverse_transform = False
                        )
        if self.differentiation is not None:
            y_true[k] = differentiator.fit_transform(y_true[k])[self.differentiation:]
            y_pred[k] = differentiator.fit_transform(y_pred[k])[self.differentiation:]

        residuals[k] = y_true[k] - y_pred[k]

    for key, value in residuals.items():
        if append and self.out_sample_residuals_[key] is not None:
            value = np.concatenate((
                        self.out_sample_residuals_[key],
                        value
                    ))
        if len(value) > 10000:
            value = rng.choice(value, size=10000, replace=False)
        self.out_sample_residuals_[key] = value

get_feature_importances

get_feature_importances(step, sort_importance=True)

Return feature importance of the model stored in the forecaster for a specific step. Since a separate model is created for each forecast time step, it is necessary to select the model from which retrieve information. Only valid when regressor stores internally the feature importances in the attribute feature_importances_ or coef_. Otherwise, it returns
None.

Parameters:

Name	Type	Description	Default
step	int	Model from which retrieve information (a separate model is created for each forecast time step). First step is 1.	required
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\direct\_forecaster_direct.py

def get_feature_importances(
    self, 
    step: int,
    sort_importance: bool = True
) -> pd.DataFrame:
    """
    Return feature importance of the model stored in the forecaster for a
    specific step. Since a separate model is created for each forecast time
    step, it is necessary to select the model from which retrieve information.
    Only valid when regressor stores internally the feature importances in
    the attribute `feature_importances_` or `coef_`. Otherwise, it returns  
    `None`.

    Parameters
    ----------
    step : int
        Model from which retrieve information (a separate model is created 
        for each forecast time step). First step is 1.
    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 isinstance(step, int):
        raise TypeError(f"`step` must be an integer. Got {type(step)}.")

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

    if (step < 1) or (step > self.steps):
        raise ValueError(
            f"The step must have a value from 1 to the maximum number of steps "
            f"({self.steps}). Got {step}."
        )

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

    n_lags = len(self.lags) if self.lags is not None else 0
    n_window_features = (
        len(self.window_features_names) if self.window_features is not None else 0
    )
    idx_columns_autoreg = np.arange(n_lags + n_window_features)
    if not self.exog_in_:
        idx_columns = idx_columns_autoreg
    else:
        n_exog = len(self.X_train_direct_exog_names_out_) / self.steps
        idx_columns_exog = (
            np.arange((step - 1) * n_exog, (step) * n_exog) + idx_columns_autoreg[-1] + 1
        )
        idx_columns = np.concatenate((idx_columns_autoreg, idx_columns_exog))

    idx_columns = [int(x) for x in idx_columns]  # Required since numpy 2.0
    feature_names = [self.X_train_features_names_out_[i].replace(f"_step_{step}", "") 
                     for i in idx_columns]

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

    return feature_importances