tsa.statespace.kalman_filter.FilterResults()
statsmodels.tsa.statespace.kalman_filter.FilterResults
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class statsmodels.tsa.statespace.kalman_filter.FilterResults(model)
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Results from applying the Kalman filter to a state space model.
Parameters: model : Representation
A Statespace representation
Attributes
nobs (int) Number of observations. k_endog (int) The dimension of the observation series. k_states (int) The dimension of the unobserved state process. k_posdef (int) The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation. dtype (dtype) Datatype of representation matrices prefix (str) BLAS prefix of representation matrices shapes (dictionary of name,tuple) A dictionary recording the shapes of each of the representation matrices as tuples. endog (array) The observation vector. design (array) The design matrix, . obs_intercept (array) The intercept for the observation equation, . obs_cov (array) The covariance matrix for the observation equation . transition (array) The transition matrix, . state_intercept (array) The intercept for the transition equation, . selection (array) The selection matrix, . state_cov (array) The covariance matrix for the state equation . missing (array of bool) An array of the same size as endog
, filled with boolean values that are True if the corresponding entry inendog
is NaN and False otherwise.nmissing (array of int) An array of size nobs
, where the ith entry is the number (between 0 andk_endog
) of NaNs in the ith row of theendog
array.time_invariant (bool) Whether or not the representation matrices are time-invariant initialization (str) Kalman filter initialization method. initial_state (array_like) The state vector used to initialize the Kalamn filter. initial_state_cov (array_like) The state covariance matrix used to initialize the Kalamn filter. filter_method (int) Bitmask representing the Kalman filtering method inversion_method (int) Bitmask representing the method used to invert the forecast error covariance matrix. stability_method (int) Bitmask representing the methods used to promote numerical stability in the Kalman filter recursions. conserve_memory (int) Bitmask representing the selected memory conservation method. tolerance (float) The tolerance at which the Kalman filter determines convergence to steady-state. loglikelihood_burn (int) The number of initial periods during which the loglikelihood is not recorded. converged (bool) Whether or not the Kalman filter converged. period_converged (int) The time period in which the Kalman filter converged. 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. llf_obs (array) The loglikelihood values at each time period. 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 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
kalman_gain
Kalman gain matrices 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.FilterResults.html