# statsmodels.tsa.statespace.kalman_filter.FilterResults.predict¶

FilterResults.predict(start=None, end=None, dynamic=None, **kwargs)[source]

In-sample and out-of-sample prediction for state space models generally

Parameters: start (int, optional) – Zero-indexed observation number at which to start forecasting, i.e., the first forecast will be at start. end (int, optional) – Zero-indexed observation number at which to end forecasting, i.e., the last forecast will be at end. dynamic (int, optional) – Offset relative to start at which to begin dynamic prediction. 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. **kwargs – If the prediction range is outside of the sample range, any of the state space representation matrices that are time-varying must have updated values provided for the out-of-sample range. For example, of obs_intercept is a time-varying component and the prediction range extends 10 periods beyond the end of the sample, a (k_endog x 10) matrix must be provided with the new intercept values. results – A PredictionResults object. kalman_filter.PredictionResults

Notes

All prediction is performed by applying the deterministic part of the measurement equation using the predicted state variables.

Out-of-sample prediction first applies the Kalman filter to missing data for the number of periods desired to obtain the predicted states.