# 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).
npredictions

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.

Type: int
start

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.

Type: int
end

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.

Type: int
nstatic

Number of in-sample static predictions.

Type: int
ndynamic

Number of in-sample dynamic predictions.

Type: int
nforecast

Number of in-sample forecasts.

Type: int
endog

The observation vector.

Type: array
design

The design matrix, $$Z$$.

Type: array
obs_intercept

The intercept for the observation equation, $$d$$.

Type: array
obs_cov

The covariance matrix for the observation equation $$H$$.

Type: array
transition

The transition matrix, $$T$$.

Type: array
state_intercept

The intercept for the transition equation, $$c$$.

Type: array
selection

The selection matrix, $$R$$.

Type: array
state_cov

The covariance matrix for the state equation $$Q$$.

Type: array
filtered_state

The filtered state vector at each time period.

Type: array
filtered_state_cov

The filtered state covariance matrix at each time period.

Type: array
predicted_state

The predicted state vector at each time period.

Type: array
predicted_state_cov

The predicted state covariance matrix at each time period.

Type: array
forecasts

The one-step-ahead forecasts of observations at each time period.

Type: array
forecasts_error

The forecast errors at each time period.

Type: array
forecasts_error_cov

The forecast error covariance matrices at each time period.

Type: array

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]) 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

Attributes

 filter_attributes kalman_gain Kalman gain matrices representation_attributes standardized_forecasts_error Standardized forecast errors