model_selection

backtesting_forecaster(forecaster, y, steps, metric, initial_train_size=None, fixed_train_size=True, gap=0, allow_incomplete_fold=True, exog=None, refit=False, interval=None, n_boot=500, random_state=123, in_sample_residuals=True, n_jobs='auto', verbose=False, show_progress=True)

Backtesting of forecaster model.

• If refit is False, the model will be trained only once using the initial_train_size first observations.
• If refit is True, the model is trained on each iteration, increasing the training set.
• If refit is an integer, the model will be trained every that number of iterations.
• If forecaster is already trained and initial_train_size is None, no initial train will be done and all data will be used to evaluate the model. However, the first len(forecaster.last_window) observations are needed to create the initial predictors, so no predictions are calculated for them.

A copy of the original forecaster is created so that it is not modified during the process.

Parameters:

Name	Type	Description	Default
forecaster	ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect	Forecaster model.	required
y	pandas Series	Training time series.	required
steps	int	Number of steps to predict.	required
metric	str, Callable, list	Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_squared_log_error'} If Callable: Function with arguments y_true, y_pred that returns a float. If list: List containing multiple strings and/or Callables.	required
initial_train_size	int	Number of samples in the initial train split. If None and forecaster is already trained, no initial train is done and all data is used to evaluate the model. However, the first len(forecaster.last_window) observations are needed to create the initial predictors, so no predictions are calculated for them. This useful to backtest the model on the same data used to train it. None is only allowed when refit is False and forecaster is already trained.	`None`
fixed_train_size	bool	If True, train size doesn't increase but moves by steps in each iteration.	`True`
gap	int	Number of samples to be excluded after the end of each training set and before the test set.	`0`
allow_incomplete_fold	bool	Last fold is allowed to have a smaller number of samples than the test_size. If False, the last fold is excluded.	`True`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s. Must have the same number of observations as y and should be aligned so that y[i] is regressed on exog[i].	`None`
refit	bool, int	Whether to re-fit the forecaster in each iteration. If refit is an integer, the Forecaster will be trained every that number of iterations.	`False`
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]. If None, no intervals are estimated.	`None`
n_boot	int	Number of bootstrapping iterations used to estimate prediction intervals.	`500`
random_state	int	Sets a seed to the random generator, so that boot intervals are always deterministic.	`123`
in_sample_residuals	bool	If True, residuals from the training data are used as proxy of prediction error to create prediction intervals. If False, out_sample_residuals are used if they are already stored inside the forecaster.	`True`
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_backtesting. New in version 0.9.0	`'auto'`
verbose	bool	Print number of folds and index of training and validation sets used for backtesting.	`False`
show_progress	bool	Whether to show a progress bar.	True

Returns:

Name	Type	Description
metrics_value	float, list	Value(s) of the metric(s).
backtest_predictions	pandas DataFrame	Value of predictions and their estimated interval if interval is not None. column pred: predictions. column lower_bound: lower bound of the interval. column upper_bound: upper bound of the interval.

Source code in skforecast\model_selection\model_selection.py

def backtesting_forecaster(
    forecaster,
    y: pd.Series,
    steps: int,
    metric: Union[str, Callable, list],
    initial_train_size: Optional[int]=None,
    fixed_train_size: bool=True,
    gap: int=0,
    allow_incomplete_fold: bool=True,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    refit: Optional[Union[bool, int]]=False,
    interval: Optional[list]=None,
    n_boot: int=500,
    random_state: int=123,
    in_sample_residuals: bool=True,
    n_jobs: Optional[Union[int, str]]='auto',
    verbose: bool=False,
    show_progress: bool=True
) -> Tuple[Union[float, list], pd.DataFrame]:
    """
    Backtesting of forecaster model.

    - If `refit` is `False`, the model will be trained only once using the 
    `initial_train_size` first observations. 
    - If `refit` is `True`, the model is trained on each iteration, increasing
    the training set. 
    - If `refit` is an `integer`, the model will be trained every that number 
    of iterations.
    - If `forecaster` is already trained and `initial_train_size` is `None`,
    no initial train will be done and all data will be used to evaluate the model.
    However, the first `len(forecaster.last_window)` observations are needed
    to create the initial predictors, so no predictions are calculated for them.

    A copy of the original forecaster is created so that it is not modified during 
    the process.

    Parameters
    ----------
    forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect
        Forecaster model.
    y : pandas Series
        Training time series.
    steps : int
        Number of steps to predict.
    metric : str, Callable, list
        Metric used to quantify the goodness of fit of the model.

            - If `string`: {'mean_squared_error', 'mean_absolute_error',
             'mean_absolute_percentage_error', 'mean_squared_log_error'}
            - If `Callable`: Function with arguments y_true, y_pred that returns 
            a float.
            - If `list`: List containing multiple strings and/or Callables.
    initial_train_size : int, default `None`
        Number of samples in the initial train split. If `None` and `forecaster` is 
        already trained, no initial train is done and all data is used to evaluate the 
        model. However, the first `len(forecaster.last_window)` observations are needed 
        to create the initial predictors, so no predictions are calculated for them. 
        This useful to backtest the model on the same data used to train it.
        `None` is only allowed when `refit` is `False` and `forecaster` is already
        trained.
    fixed_train_size : bool, default `True`
        If True, train size doesn't increase but moves by `steps` in each iteration.
    gap : int, default `0`
        Number of samples to be excluded after the end of each training set and 
        before the test set.
    allow_incomplete_fold : bool, default `True`
        Last fold is allowed to have a smaller number of samples than the 
        `test_size`. If `False`, the last fold is excluded.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s. Must have the same
        number of observations as `y` and should be aligned so that y[i] is
        regressed on exog[i].
    refit : bool, int, default `False`
        Whether to re-fit the forecaster in each iteration. If `refit` is an integer, 
        the Forecaster will be trained every that number of iterations.
    interval : list, default `None`
        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]`. If `None`, no
        intervals are estimated.
    n_boot : int, default `500`
        Number of bootstrapping iterations used to estimate prediction
        intervals.
    random_state : int, default `123`
        Sets a seed to the random generator, so that boot intervals are always 
        deterministic.
    in_sample_residuals : bool, default `True`
        If `True`, residuals from the training data are used as proxy of prediction 
        error to create prediction intervals.  If `False`, out_sample_residuals 
        are used if they are already stored inside the forecaster.
    n_jobs : int, 'auto', default `'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_backtesting.
        **New in version 0.9.0**
    verbose : bool, default `False`
        Print number of folds and index of training and validation sets used 
        for backtesting.
    show_progress: bool, default `True`
        Whether to show a progress bar.

    Returns
    -------
    metrics_value : float, list
        Value(s) of the metric(s).
    backtest_predictions : pandas DataFrame
        Value of predictions and their estimated interval if `interval` is not `None`.

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

    """

    if type(forecaster).__name__ not in ['ForecasterAutoreg', 
                                         'ForecasterAutoregCustom', 
                                         'ForecasterAutoregDirect']:
        raise TypeError(
            ("`forecaster` must be of type `ForecasterAutoreg`, `ForecasterAutoregCustom` "
             "or `ForecasterAutoregDirect`, for all other types of forecasters "
             "use the functions available in the other `model_selection` modules.")
        )

    check_backtesting_input(
        forecaster            = forecaster,
        steps                 = steps,
        metric                = metric,
        y                     = y,
        initial_train_size    = initial_train_size,
        fixed_train_size      = fixed_train_size,
        gap                   = gap,
        allow_incomplete_fold = allow_incomplete_fold,
        refit                 = refit,
        interval              = interval,
        n_boot                = n_boot,
        random_state          = random_state,
        in_sample_residuals   = in_sample_residuals,
        n_jobs                = n_jobs,
        verbose               = verbose,
        show_progress         = show_progress
    )

    if type(forecaster).__name__ == 'ForecasterAutoregDirect' and \
       forecaster.steps < steps + gap:
        raise ValueError(
            ("When using a ForecasterAutoregDirect, the combination of steps "
             f"+ gap ({steps+gap}) cannot be greater than the `steps` parameter "
             f"declared when the forecaster is initialized ({forecaster.steps}).")
        )

    metrics_values, backtest_predictions = _backtesting_forecaster(
        forecaster            = forecaster,
        y                     = y,
        steps                 = steps,
        metric                = metric,
        initial_train_size    = initial_train_size,
        fixed_train_size      = fixed_train_size,
        gap                   = gap,
        allow_incomplete_fold = allow_incomplete_fold,
        exog                  = exog,
        refit                 = refit,
        interval              = interval,
        n_boot                = n_boot,
        random_state          = random_state,
        in_sample_residuals   = in_sample_residuals,
        n_jobs                = n_jobs,
        verbose               = verbose,
        show_progress         = show_progress
    )

    return metrics_values, backtest_predictions

grid_search_forecaster(forecaster, y, param_grid, steps, metric, initial_train_size, fixed_train_size=True, gap=0, allow_incomplete_fold=True, exog=None, lags_grid=None, refit=False, return_best=True, n_jobs='auto', verbose=True, show_progress=True)

Exhaustive search over specified parameter values for a Forecaster object. Validation is done using time series backtesting.

Parameters:

Name	Type	Description	Default
forecaster	ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect	Forecaster model.	required
y	pandas Series	Training time series.	required
param_grid	dict	Dictionary with parameters names (str) as keys and lists of parameter settings to try as values.	required
steps	int	Number of steps to predict.	required
metric	str, Callable, list	Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_squared_log_error'} If Callable: Function with arguments y_true, y_pred that returns a float. If list: List containing multiple strings and/or Callables.	required
initial_train_size	int	Number of samples in the initial train split.	required
fixed_train_size	bool	If True, train size doesn't increase but moves by steps in each iteration.	`True`
gap	int	Number of samples to be excluded after the end of each training set and before the test set.	`0`
allow_incomplete_fold	bool	Last fold is allowed to have a smaller number of samples than the test_size. If False, the last fold is excluded.	`True`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s. Must have the same number of observations as y and should be aligned so that y[i] is regressed on exog[i].	`None`
lags_grid	list of int, lists, numpy ndarray or range	Lists of lags to try. Only used if forecaster is an instance of ForecasterAutoreg or ForecasterAutoregDirect.	`None`
refit	bool, int	Whether to re-fit the forecaster in each iteration. If refit is an integer, the Forecaster will be trained every that number of iterations.	`False`
return_best	bool	Refit the forecaster using the best found parameters on the whole data.	`True`
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_backtesting. New in version 0.9.0	`'auto'`
verbose	bool	Print number of folds used for cv or backtesting.	`True`
show_progress	bool	Whether to show a progress bar.	True

Returns:

Name	Type	Description
results	pandas DataFrame	Results for each combination of parameters. column lags: lags configuration for each iteration. column params: parameters configuration for each iteration. column metric: metric value estimated for each iteration. additional n columns with param = value.

Source code in skforecast\model_selection\model_selection.py

def grid_search_forecaster(
    forecaster,
    y: pd.Series,
    param_grid: dict,
    steps: int,
    metric: Union[str, Callable, list],
    initial_train_size: int,
    fixed_train_size: bool=True,
    gap: int=0,
    allow_incomplete_fold: bool=True,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    lags_grid: Optional[list]=None,
    refit: Optional[Union[bool, int]]=False,
    return_best: bool=True,
    n_jobs: Optional[Union[int, str]]='auto',
    verbose: bool=True,
    show_progress: bool=True
) -> pd.DataFrame:
    """
    Exhaustive search over specified parameter values for a Forecaster object.
    Validation is done using time series backtesting.

    Parameters
    ----------
    forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect
        Forecaster model.
    y : pandas Series
        Training time series. 
    param_grid : dict
        Dictionary with parameters names (`str`) as keys and lists of parameter
        settings to try as values.
    steps : int
        Number of steps to predict.
    metric : str, Callable, list
        Metric used to quantify the goodness of fit of the model.

            - If `string`: {'mean_squared_error', 'mean_absolute_error',
             'mean_absolute_percentage_error', 'mean_squared_log_error'}
            - If `Callable`: Function with arguments y_true, y_pred that returns 
            a float.
            - If `list`: List containing multiple strings and/or Callables.
    initial_train_size : int 
        Number of samples in the initial train split.
    fixed_train_size : bool, default `True`
        If True, train size doesn't increase but moves by `steps` in each iteration.
    gap : int, default `0`
        Number of samples to be excluded after the end of each training set and 
        before the test set.
    allow_incomplete_fold : bool, default `True`
        Last fold is allowed to have a smaller number of samples than the 
        `test_size`. If `False`, the last fold is excluded.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s. Must have the same
        number of observations as `y` and should be aligned so that y[i] is
        regressed on exog[i].
    lags_grid : list of int, lists, numpy ndarray or range, default `None`
        Lists of `lags` to try. Only used if forecaster is an instance of 
        `ForecasterAutoreg` or `ForecasterAutoregDirect`.
    refit : bool, int, default `False`
        Whether to re-fit the forecaster in each iteration. If `refit` is an integer, 
        the Forecaster will be trained every that number of iterations.
    return_best : bool, default `True`
        Refit the `forecaster` using the best found parameters on the whole data.
    n_jobs : int, 'auto', default `'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_backtesting.
        **New in version 0.9.0**
    verbose : bool, default `True`
        Print number of folds used for cv or backtesting.
    show_progress: bool, default `True`
        Whether to show a progress bar.

    Returns
    -------
    results : pandas DataFrame
        Results for each combination of parameters.

            - column lags: lags configuration for each iteration.
            - column params: parameters configuration for each iteration.
            - column metric: metric value estimated for each iteration.
            - additional n columns with param = value.

    """

    param_grid = list(ParameterGrid(param_grid))

    results = _evaluate_grid_hyperparameters(
        forecaster            = forecaster,
        y                     = y,
        param_grid            = param_grid,
        steps                 = steps,
        metric                = metric,
        initial_train_size    = initial_train_size,
        fixed_train_size      = fixed_train_size,
        gap                   = gap,
        allow_incomplete_fold = allow_incomplete_fold,
        exog                  = exog,
        lags_grid             = lags_grid,
        refit                 = refit,
        return_best           = return_best,
        n_jobs                = n_jobs,
        verbose               = verbose,
        show_progress         = show_progress
    )

    return results

random_search_forecaster(forecaster, y, param_distributions, steps, metric, initial_train_size, fixed_train_size=True, gap=0, allow_incomplete_fold=True, exog=None, lags_grid=None, refit=False, n_iter=10, random_state=123, return_best=True, n_jobs='auto', verbose=True, show_progress=True)

Random search over specified parameter values or distributions for a Forecaster object. Validation is done using time series backtesting.

Parameters:

Name	Type	Description	Default
forecaster	ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect	Forecaster model.	required
y	pandas Series	Training time series.	required
param_distributions	dict	Dictionary with parameters names (str) as keys and distributions or lists of parameters to try.	required
steps	int	Number of steps to predict.	required
metric	str, Callable, list	Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_squared_log_error'} If Callable: Function with arguments y_true, y_pred that returns a float. If list: List containing multiple strings and/or Callables.	required
initial_train_size	int	Number of samples in the initial train split.	required
fixed_train_size	bool	If True, train size doesn't increase but moves by steps in each iteration.	`True`
gap	int	Number of samples to be excluded after the end of each training set and before the test set.	`0`
allow_incomplete_fold	bool	Last fold is allowed to have a smaller number of samples than the test_size. If False, the last fold is excluded.	`True`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s. Must have the same number of observations as y and should be aligned so that y[i] is regressed on exog[i].	`None`
lags_grid	list of int, lists, numpy ndarray or range	Lists of lags to try. Only used if forecaster is an instance of ForecasterAutoreg or ForecasterAutoregDirect.	`None`
refit	bool, int	Whether to re-fit the forecaster in each iteration. If refit is an integer, the Forecaster will be trained every that number of iterations.	`False`
n_iter	int	Number of parameter settings that are sampled per lags configuration. n_iter trades off runtime vs quality of the solution.	`10`
random_state	int	Sets a seed to the random sampling for reproducible output.	`123`
return_best	bool	Refit the forecaster using the best found parameters on the whole data.	`True`
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_backtesting. New in version 0.9.0	`'auto'`
verbose	bool	Print number of folds used for cv or backtesting.	`True`
show_progress	bool	Whether to show a progress bar.	True

Returns:

Name	Type	Description
results	pandas DataFrame	Results for each combination of parameters. column lags: lags configuration for each iteration. column params: parameters configuration for each iteration. column metric: metric value estimated for each iteration. additional n columns with param = value.

Source code in skforecast\model_selection\model_selection.py

def random_search_forecaster(
    forecaster,
    y: pd.Series,
    param_distributions: dict,
    steps: int,
    metric: Union[str, Callable, list],
    initial_train_size: int,
    fixed_train_size: bool=True,
    gap: int=0,
    allow_incomplete_fold: bool=True,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    lags_grid: Optional[list]=None,
    refit: Optional[Union[bool, int]]=False,
    n_iter: int=10,
    random_state: int=123,
    return_best: bool=True,
    n_jobs: Optional[Union[int, str]]='auto',
    verbose: bool=True,
    show_progress: bool=True
) -> pd.DataFrame:
    """
    Random search over specified parameter values or distributions for a Forecaster 
    object. Validation is done using time series backtesting.

    Parameters
    ----------
    forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect
        Forecaster model.
    y : pandas Series
        Training time series. 
    param_distributions : dict
        Dictionary with parameters names (`str`) as keys and 
        distributions or lists of parameters to try.
    steps : int
        Number of steps to predict.
    metric : str, Callable, list
        Metric used to quantify the goodness of fit of the model.

            - If `string`: {'mean_squared_error', 'mean_absolute_error',
             'mean_absolute_percentage_error', 'mean_squared_log_error'}
            - If `Callable`: Function with arguments y_true, y_pred that returns 
            a float.
            - If `list`: List containing multiple strings and/or Callables.
    initial_train_size : int 
        Number of samples in the initial train split.
    fixed_train_size : bool, default `True`
        If True, train size doesn't increase but moves by `steps` in each iteration.
    gap : int, default `0`
        Number of samples to be excluded after the end of each training set and 
        before the test set.
    allow_incomplete_fold : bool, default `True`
        Last fold is allowed to have a smaller number of samples than the 
        `test_size`. If `False`, the last fold is excluded.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s. Must have the same
        number of observations as `y` and should be aligned so that y[i] is
        regressed on exog[i]. 
    lags_grid : list of int, lists, numpy ndarray or range, default `None`
        Lists of `lags` to try. Only used if forecaster is an instance of 
        `ForecasterAutoreg` or `ForecasterAutoregDirect`.
    refit : bool, int, default `False`
        Whether to re-fit the forecaster in each iteration. If `refit` is an integer, 
        the Forecaster will be trained every that number of iterations.
    n_iter : int, default `10`
        Number of parameter settings that are sampled per lags configuration. 
        n_iter trades off runtime vs quality of the solution.
    random_state : int, default `123`
        Sets a seed to the random sampling for reproducible output.
    return_best : bool, default `True`
        Refit the `forecaster` using the best found parameters on the whole data.
    n_jobs : int, 'auto', default `'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_backtesting.
        **New in version 0.9.0**
    verbose : bool, default `True`
        Print number of folds used for cv or backtesting.
    show_progress: bool, default `True`
        Whether to show a progress bar.

    Returns
    -------
    results : pandas DataFrame
        Results for each combination of parameters.

            - column lags: lags configuration for each iteration.
            - column params: parameters configuration for each iteration.
            - column metric: metric value estimated for each iteration.
            - additional n columns with param = value.

    """

    param_grid = list(ParameterSampler(param_distributions, n_iter=n_iter, random_state=random_state))

    results = _evaluate_grid_hyperparameters(
        forecaster            = forecaster,
        y                     = y,
        param_grid            = param_grid,
        steps                 = steps,
        metric                = metric,
        initial_train_size    = initial_train_size,
        fixed_train_size      = fixed_train_size,
        gap                   = gap,
        allow_incomplete_fold = allow_incomplete_fold,
        exog                  = exog,
        lags_grid             = lags_grid,
        refit                 = refit,
        return_best           = return_best,
        n_jobs                = n_jobs,
        verbose               = verbose,
        show_progress         = show_progress
    )

    return results

bayesian_search_forecaster(forecaster, y, search_space, steps, metric, initial_train_size, fixed_train_size=True, gap=0, allow_incomplete_fold=True, exog=None, lags_grid=None, refit=False, n_trials=10, random_state=123, return_best=True, n_jobs='auto', verbose=True, show_progress=True, engine='optuna', kwargs_create_study={}, kwargs_study_optimize={})

Bayesian optimization for a Forecaster object using time series backtesting and optuna library.

Parameters:

Name	Type	Description	Default
forecaster	ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect	Forecaster model.	required
y	pandas Series	Training time series.	required
search_space	Callable(optuna)	Function with argument trial which returns a dictionary with parameters names (str) as keys and Trial object from optuna (trial.suggest_float, trial.suggest_int, trial.suggest_categorical) as values.	required
steps	int	Number of steps to predict.	required
metric	str, Callable, list	Metric used to quantify the goodness of fit of the model. If string: {'mean_squared_error', 'mean_absolute_error', 'mean_absolute_percentage_error', 'mean_squared_log_error'} If Callable: Function with arguments y_true, y_pred that returns a float. If list: List containing multiple strings and/or Callables.	required
initial_train_size	int	Number of samples in the initial train split.	required
fixed_train_size	bool	If True, train size doesn't increase but moves by steps in each iteration.	`True`
gap	int	Number of samples to be excluded after the end of each training set and before the test set.	`0`
allow_incomplete_fold	bool	Last fold is allowed to have a smaller number of samples than the test_size. If False, the last fold is excluded.	`True`
exog	pandas Series, pandas DataFrame	Exogenous variable/s included as predictor/s. Must have the same number of observations as y and should be aligned so that y[i] is regressed on exog[i].	`None`
lags_grid	list of int, lists, numpy ndarray or range	Lists of lags to try. Only used if forecaster is an instance of ForecasterAutoreg or ForecasterAutoregDirect.	`None`
refit	bool, int	Whether to re-fit the forecaster in each iteration. If refit is an integer, the Forecaster will be trained every that number of iterations.	`False`
n_trials	int	Number of parameter settings that are sampled in each lag configuration.	`10`
random_state	int	Sets a seed to the sampling for reproducible output.	`123`
return_best	bool	Refit the forecaster using the best found parameters on the whole data.	`True`
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_backtesting. New in version 0.9.0	`'auto'`
verbose	bool	Print number of folds used for cv or backtesting.	`True`
show_progress	bool	Whether to show a progress bar.	True
engine	str	Bayesian optimization runs through the optuna library.	`'optuna'`
kwargs_create_study	dict	Only applies to engine='optuna'. Keyword arguments (key, value mappings) to pass to optuna.create_study.	`{'direction':'minimize', 'sampler':TPESampler(seed=123)}`
kwargs_study_optimize	dict	Only applies to engine='optuna'. Other keyword arguments (key, value mappings) to pass to study.optimize().	`{}`

Returns:

Name	Type	Description
results	pandas DataFrame	Results for each combination of parameters. column lags: lags configuration for each iteration. column params: parameters configuration for each iteration. column metric: metric value estimated for each iteration. additional n columns with param = value.
results_opt_best	optuna object (optuna)	The best optimization result returned as a FrozenTrial optuna object.

Source code in skforecast\model_selection\model_selection.py

def bayesian_search_forecaster(
    forecaster,
    y: pd.Series,
    search_space: Callable,
    steps: int,
    metric: Union[str, Callable, list],
    initial_train_size: int,
    fixed_train_size: bool=True,
    gap: int=0,
    allow_incomplete_fold: bool=True,
    exog: Optional[Union[pd.Series, pd.DataFrame]]=None,
    lags_grid: Optional[list]=None,
    refit: Optional[Union[bool, int]]=False,
    n_trials: int=10,
    random_state: int=123,
    return_best: bool=True,
    n_jobs: Optional[Union[int, str]]='auto',
    verbose: bool=True,
    show_progress: bool=True,
    engine: str='optuna',
    kwargs_create_study: dict={},
    kwargs_study_optimize: dict={}
) -> Tuple[pd.DataFrame, object]:
    """
    Bayesian optimization for a Forecaster object using time series backtesting and 
    optuna library.

    Parameters
    ----------
    forecaster : ForecasterAutoreg, ForecasterAutoregCustom, ForecasterAutoregDirect
        Forecaster model.
    y : pandas Series
        Training time series. 
    search_space : Callable (optuna)
        Function with argument `trial` which returns a dictionary with parameters names 
        (`str`) as keys and Trial object from optuna (trial.suggest_float, 
        trial.suggest_int, trial.suggest_categorical) as values.
    steps : int
        Number of steps to predict.
    metric : str, Callable, list
        Metric used to quantify the goodness of fit of the model.

            - If `string`: {'mean_squared_error', 'mean_absolute_error',
             'mean_absolute_percentage_error', 'mean_squared_log_error'}
            - If `Callable`: Function with arguments y_true, y_pred that returns 
            a float.
            - If `list`: List containing multiple strings and/or Callables.
    initial_train_size : int 
        Number of samples in the initial train split.
    fixed_train_size : bool, default `True`
        If True, train size doesn't increase but moves by `steps` in each iteration.
    gap : int, default `0`
        Number of samples to be excluded after the end of each training set and 
        before the test set.
    allow_incomplete_fold : bool, default `True`
        Last fold is allowed to have a smaller number of samples than the 
        `test_size`. If `False`, the last fold is excluded.
    exog : pandas Series, pandas DataFrame, default `None`
        Exogenous variable/s included as predictor/s. Must have the same
        number of observations as `y` and should be aligned so that y[i] is
        regressed on exog[i]. 
    lags_grid : list of int, lists, numpy ndarray or range, default `None`
        Lists of `lags` to try. Only used if forecaster is an instance of 
        `ForecasterAutoreg` or `ForecasterAutoregDirect`.
    refit : bool, int, default `False`
        Whether to re-fit the forecaster in each iteration. If `refit` is an integer, 
        the Forecaster will be trained every that number of iterations.
    n_trials : int, default `10`
        Number of parameter settings that are sampled in each lag configuration.
    random_state : int, default `123`
        Sets a seed to the sampling for reproducible output.
    return_best : bool, default `True`
        Refit the `forecaster` using the best found parameters on the whole data.
    n_jobs : int, 'auto', default `'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_backtesting.
        **New in version 0.9.0**
    verbose : bool, default `True`
        Print number of folds used for cv or backtesting.
    show_progress: bool, default `True`
        Whether to show a progress bar.
    engine : str, default `'optuna'`
        Bayesian optimization runs through the optuna library.
    kwargs_create_study : dict, default `{'direction':'minimize', 'sampler':TPESampler(seed=123)}`
        Only applies to engine='optuna'. Keyword arguments (key, value mappings) 
        to pass to optuna.create_study.
    kwargs_study_optimize : dict, default `{}`
        Only applies to engine='optuna'. Other keyword arguments (key, value mappings) 
        to pass to study.optimize().

    Returns
    -------
    results : pandas DataFrame
        Results for each combination of parameters.

            - column lags: lags configuration for each iteration.
            - column params: parameters configuration for each iteration.
            - column metric: metric value estimated for each iteration.
            - additional n columns with param = value.
    results_opt_best : optuna object (optuna)  
        The best optimization result returned as a FrozenTrial optuna object.

    """

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

    if engine == 'skopt':
        warnings.warn(
            ("The engine 'skopt' for `bayesian_search_forecaster` is deprecated "
             "in favor of 'optuna' engine. To continue using it, use skforecast "
             "0.6.0. The optimization will be performed using the 'optuna' engine.")
        )
        engine = 'optuna'

    if engine not in ['optuna']:
        raise ValueError(
            f"""`engine` only allows 'optuna', got {engine}."""
        )

    results, results_opt_best = _bayesian_search_optuna(
                                    forecaster            = forecaster,
                                    y                     = y,
                                    exog                  = exog,
                                    lags_grid             = lags_grid,
                                    search_space          = search_space,
                                    steps                 = steps,
                                    metric                = metric,
                                    refit                 = refit,
                                    initial_train_size    = initial_train_size,
                                    fixed_train_size      = fixed_train_size,
                                    gap                   = gap,
                                    allow_incomplete_fold = allow_incomplete_fold,
                                    n_trials              = n_trials,
                                    random_state          = random_state,
                                    return_best           = return_best,
                                    n_jobs                = n_jobs,
                                    verbose               = verbose,
                                    show_progress         = show_progress,
                                    kwargs_create_study   = kwargs_create_study,
                                    kwargs_study_optimize = kwargs_study_optimize
                                )

    return results, results_opt_best