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