statsmodels.tsa.statespace.kalman_filter.PredictionResults¶
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class
statsmodels.tsa.statespace.kalman_filter.
PredictionResults
(results, start, end, nstatic, ndynamic, nforecast)[source]¶ Results of in-sample and out-of-sample prediction for state space models generally
Parameters: results : FilterResults
Output from filtering, corresponding to the prediction desired
start : int
Zero-indexed observation number at which to start forecasting, i.e., the first forecast will be at start.
end : int
Zero-indexed observation number at which to end forecasting, i.e., the last forecast will be at end.
nstatic : int
Number of in-sample static predictions (these are always the first elements of the prediction output).
ndynamic : int
Number of in-sample dynamic predictions (these always follow the static predictions directly, and are directly followed by the forecasts).
nforecast : int
Number of in-sample forecasts (these always follow the dynamic predictions directly).
Notes
The provided ranges must be conformable, meaning that it must be that end - start == nstatic + ndynamic + nforecast.
This class is essentially a view to the FilterResults object, but returning the appropriate ranges for everything.
Attributes
npredictions (int) Number of observations in the predicted series; this is not necessarily the same as the number of observations in the original model from which prediction was performed. start (int) Zero-indexed observation number at which to start prediction, i.e., the first predict will be at start; this is relative to the original model from which prediction was performed. end (int) Zero-indexed observation number at which to end prediction, i.e., the last predict will be at end; this is relative to the original model from which prediction was performed. nstatic (int) Number of in-sample static predictions. ndynamic (int) Number of in-sample dynamic predictions. nforecast (int) Number of in-sample forecasts. endog (array) The observation vector. design (array) The design matrix, \(Z\). obs_intercept (array) The intercept for the observation equation, \(d\). obs_cov (array) The covariance matrix for the observation equation \(H\). transition (array) The transition matrix, \(T\). state_intercept (array) The intercept for the transition equation, \(c\). selection (array) The selection matrix, \(R\). state_cov (array) The covariance matrix for the state equation \(Q\). filtered_state (array) The filtered state vector at each time period. filtered_state_cov (array) The filtered state covariance matrix at each time period. predicted_state (array) The predicted state vector at each time period. predicted_state_cov (array) The predicted state covariance matrix at each time period. forecasts (array) The one-step-ahead forecasts of observations at each time period. forecasts_error (array) The forecast errors at each time period. forecasts_error_cov (array) The forecast error covariance matrices at each time period. Methods
predict
([start, end, dynamic])In-sample and out-of-sample prediction for state space models generally update_filter
(kalman_filter)Update the filter results update_representation
(model[, only_options])Update the results to match a given model