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

filter_attributes
kalman_gain Kalman gain matrices
representation_attributes
standardized_forecasts_error Standardized forecast errors