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: |
|
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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
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 one-step-ahead 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.
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.
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 |
filter_attributes | |
kalman_gain | Kalman gain matrices |
representation_attributes | |
standardized_forecasts_error | Standardized forecast errors |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.kalman_filter.PredictionResults.html