statsmodels.tsa.statespace.kalman_filter.PredictionResults¶

class
statsmodels.tsa.statespace.kalman_filter.
PredictionResults
(results, start, end, nstatic, ndynamic, nforecast)[source]¶ Results of insample and outofsample prediction for state space models generally
Parameters:  results (FilterResults) – Output from filtering, corresponding to the prediction desired
 start (int) – Zeroindexed observation number at which to start forecasting, i.e., the first forecast will be at start.
 end (int) – Zeroindexed observation number at which to end forecasting, i.e., the last forecast will be at end.
 nstatic (int) – Number of insample static predictions (these are always the first elements of the prediction output).
 ndynamic (int) – Number of insample dynamic predictions (these always follow the static predictions directly, and are directly followed by the forecasts).
 nforecast (int) – Number of insample forecasts (these always follow the dynamic predictions directly).

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 – Zeroindexed 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 – Zeroindexed 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 insample static predictions.

ndynamic
¶ int – Number of insample dynamic predictions.

nforecast
¶ int – Number of insample 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 onestepahead 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.
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.
Methods
predict
([start, end, dynamic])Insample and outofsample 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 Attributes
filter_attributes
kalman_gain
Kalman gain matrices representation_attributes
standardized_forecasts_error
Standardized forecast errors