statsmodels.tsa.statespace.kalman_filter.PredictionResults¶
-
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
(ndarray) The observation vector.
design
(ndarray) The design matrix, \(Z\).
obs_intercept
(ndarray) The intercept for the observation equation, \(d\).
obs_cov
(ndarray) The covariance matrix for the observation equation \(H\).
transition
(ndarray) The transition matrix, \(T\).
state_intercept
(ndarray) The intercept for the transition equation, \(c\).
selection
(ndarray) The selection matrix, \(R\).
state_cov
(ndarray) The covariance matrix for the state equation \(Q\).
filtered_state
(ndarray) The filtered state vector at each time period.
filtered_state_cov
(ndarray) The filtered state covariance matrix at each time period.
predicted_state
(ndarray) The predicted state vector at each time period.
predicted_state_cov
(ndarray) The predicted state covariance matrix at each time period.
forecasts
(ndarray) The one-step-ahead forecasts of observations at each time period.
forecasts_error
(ndarray) The forecast errors at each time period.
forecasts_error_cov
(ndarray) The forecast error covariance matrices at each time period.
Methods
clear
()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
Methods
clear
()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
Properties
Kalman gain matrices
Standardized forecast errors