pycaret.time_series.predict_model#

pycaret.time_series.predict_model(estimator, fh=None, X=None, return_pred_int=False, alpha: float | None = None, coverage: float | List[float] = 0.9, round: int = 4, verbose: bool = True) → DataFrame[source]#

This function forecast using a trained model. When fh is None, it forecasts using the same forecast horizon used during the training.

Example

>>> from pycaret.datasets import get_data
>>> airline = get_data('airline')
>>> from pycaret.time_series import *
>>> exp_name = setup(data = airline,  fh = 12)
>>> arima = create_model('arima')
>>> pred_holdout = predict_model(arima)
>>> pred_unseen = predict_model(finalize_model(arima), fh = 24)

estimator: sktime compatible object: Trained model object
fh: int, default = None: Number of points from the last date of training to forecast. When fh is None, it forecasts using the same forecast horizon used during the training.
X: pd.DataFrame, default = None: Exogenous Variables to be used for prediction. Before finalizing the estimator, X need not be passed even when the estimator is built using exogenous variables (since this is taken care of internally by using the exogenous variables from test split). When estimator has been finalized and estimator used exogenous variables, then X must be passed.
return_pred_int: bool, default = False: When set to True, it returns lower bound and upper bound prediction interval, in addition to the point prediction.
alpha: Optional[float], default = None: The alpha (quantile) value to use for the point predictions. Refer to the “point_alpha” description in the setup docstring for details.
coverage: Union[float, List[float]], default = 0.9: The coverage to be used for prediction intervals. Refer to the “coverage” description in the setup docstring for details.
round: int, default = 4: Number of decimal places to round predictions to.
verbose: bool, default = True: When set to False, holdout score grid is not printed.

Returns:: pandas.DataFrame