class statsmodels.tsa.statespace.kalman_filter.FilterResults(model)
[source]
Results from applying the Kalman filter to a state space model.
Parameters: | model (Representation) – A Statespace representation |
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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, \(Z\).
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 \(Q\).
missing
array of bool – An array of the same size as endog
, filled with boolean values that are True if the corresponding entry in endog
is NaN and False otherwise.
nmissing
array of int – An array of size nobs
, where the ith entry is the number (between 0 and k_endog
) of NaNs in the ith row of the endog
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.
filter_timing
int – Whether or not to use the alternate timing convention.
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.
kalman_gain
array – The Kalman gain 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.
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 |
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