utils.validation.check_predict_input

utils.validation.check_predict_input(
    forecaster_name,
    steps,
    is_fitted,
    exog_in_,
    index_type_,
    index_freq_,
    window_size,
    last_window,
    last_window_exog=None,
    exog=None,
    exog_names_in_=None,
    interval=None,
    alpha=None,
    max_step=None,
    levels=None,
    levels_forecaster=None,
    series_names_in_=None,
    encoding=None,
)

Check all inputs of predict method. This is a helper function to validate that inputs used in predict method match attributes of a forecaster already trained.

Parameters

Name	Type	Description	Default
forecaster_name	str	str Forecaster name.	required
steps	Union[int, List[int]]	int, list Number of future steps predicted.	required
is_fitted	bool	bool Tag to identify if the estimator has been fitted (trained).	required
exog_in_	bool	bool If the forecaster has been trained using exogenous variable/s.	required
index_type_	type	type Type of index of the input used in training.	required
index_freq_	str	str Frequency of Index of the input used in training.	required
window_size	int	int Size of the window needed to create the predictors. It is equal to max_lag.	required
last_window	Optional[Union[pd.Series, pd.DataFrame]]	pandas Series, pandas DataFrame, None Values of the series used to create the predictors (lags) need in the first iteration of prediction (t + 1).	required
last_window_exog	Optional[Union[pd.Series, pd.DataFrame]]	pandas Series, pandas DataFrame, default None Values of the exogenous variables aligned with last_window in ForecasterStats predictions.	None
exog	Optional[Union[pd.Series, pd.DataFrame, Dict[str, Union[pd.Series, pd.DataFrame]]]]	pandas Series, pandas DataFrame, dict, default None Exogenous variable/s included as predictor/s.	None
exog_names_in_	Optional[List[str]]	list, default None Names of the exogenous variables used during training.	None
interval	Optional[List[float]]	list, tuple, 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].	None
alpha	Optional[float]	float, default None The confidence intervals used in ForecasterStats are (1 - alpha) %.	None
max_step	Optional[int]	int, default None Maximum number of steps allowed (ForecasterDirect and ForecasterDirectMultiVariate).	None
levels	Optional[Union[str, List[str]]]	str, list, default None Time series to be predicted (ForecasterRecursiveMultiSeries and ForecasterRnn). |None| | levels_forecaster | [Optional](typing.Optional)\[[Union](typing.Union)\[[str](str), [List](typing.List)\[[str](str)\]\]\] | str, list, default None Time series used as output data of a multiseries problem in a RNN problem (ForecasterRnn). |None| | series_names_in_ | [Optional](typing.Optional)\[[List](typing.List)\[[str](str)\]\] | list, default None Names of the columns used during fit (ForecasterRecursiveMultiSeries,ForecasterDirectMultiVariateandForecasterRnn). |None| | encoding | [Optional](typing.Optional)\[[str](str)\] | str, default None Encoding used to identify the different series (ForecasterRecursiveMultiSeries). |None`

Returns

Name	Type	Description
	None	None