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 prediction, i.e., the first prediction will be at start.
- end
int,optional Zero-indexed observation number at which to end prediction, i.e., the last prediction 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, predicted 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.
- start
- Returns:
- results
kalman_filter.PredictionResults A PredictionResults object.
- results
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.