statsmodels.tsa.statespace.structural.UnobservedComponentsResults.get_prediction

UnobservedComponentsResults.get_prediction(start=None, end=None, dynamic=False, index=None, exog=None, **kwargs)

In-sample prediction and out-of-sample forecasting

Parameters:

• start (int, str, or datetime, optional) – Zero-indexed observation number at which to start forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. Default is the the zeroth observation.
• end (int, str, or datetime, optional) – Zero-indexed observation number at which to end forecasting, ie., the first forecast is start. Can also be a date string to parse or a datetime type. However, if the dates index does not have a fixed frequency, end must be an integer index if you want out of sample prediction. Default is the last observation in the sample.
• exog (array_like, optional) – If the model includes exogenous regressors, you must provide exactly enough out-of-sample values for the exogenous variables if end is beyond the last observation in the sample.
• dynamic (boolean, int, str, or datetime, optional) – Integer offset relative to start at which to begin dynamic prediction. Can also be an absolute date string to parse or a datetime type (these are not interpreted as offsets). Prior to this observation, true endogenous values will be used for prediction; starting with this observation and continuing through the end of prediction, forecasted endogenous values will be used instead.
• full_results (boolean, optional) – If True, returns a FilterResults instance; if False returns a tuple with forecasts, the forecast errors, and the forecast error covariance matrices. Default is False.
• **kwargs – Additional arguments may required for forecasting beyond the end of the sample. See FilterResults.predict for more details.

Returns:

forecast – Array of out of sample forecasts.

Return type:

array