class statsmodels.tsa.statespace.kalman_filter.KalmanFilter(k_endog, k_states, k_posdef=None, loglikelihood_burn=0, tolerance=1e19, results_class=None, kalman_filter_classes=None, **kwargs)
[source]
State space representation of a time series process, with Kalman filter
Parameters: 


There are several types of options available for controlling the Kalman filter operation. All options are internally held as bitmasks, but can be manipulated by setting class attributes, which act like boolean flags. For more information, see the set_*
class method documentation. The options are:
The filter_method
and inversion_method
options intentionally allow the possibility that multiple methods will be indicated. In the case that multiple methods are selected, the underlying Kalman filter will attempt to select the optional method given the input data.
For example, it may be that INVERT_UNIVARIATE and SOLVE_CHOLESKY are indicated (this is in fact the default case). In this case, if the endogenous vector is 1dimensional (k_endog
= 1), then INVERT_UNIVARIATE is used and inversion reduces to simple division, and if it has a larger dimension, the Cholesky decomposition along with linear solving (rather than explicit matrix inversion) is used. If only SOLVE_CHOLESKY had been set, then the Cholesky decomposition method would always be used, even in the case of 1dimensional data.
bind (endog)  Bind data to the statespace representation 
filter ([filter_method, inversion_method, …])  Apply the Kalman filter to the statespace model. 
impulse_responses ([steps, impulse, …])  Impulse response function 
initialize_approximate_diffuse ([variance])  Initialize the statespace model with approximate diffuse values. 
initialize_known (initial_state, …)  Initialize the statespace model with known distribution for initial state. 
initialize_stationary ()  Initialize the statespace model as stationary. 
loglike (**kwargs)  Calculate the loglikelihood associated with the statespace model. 
loglikeobs (**kwargs)  Calculate the loglikelihood for each observation associated with the statespace model. 
set_conserve_memory ([conserve_memory])  Set the memory conservation method 
set_filter_method ([filter_method])  Set the filtering method 
set_filter_timing ([alternate_timing])  Set the filter timing convention 
set_inversion_method ([inversion_method])  Set the inversion method 
set_stability_method ([stability_method])  Set the numerical stability method 
simulate (nsimulations[, measurement_shocks, …])  Simulate a new time series following the state space model 
conserve_memory  (int) Memory conservation bitmask. 
design  
dtype  (dtype) Datatype of currently active representation matrices 
endog  
filter_augmented  (bool) Flag for augmented Kalman filtering. 
filter_collapsed  (bool) Flag for Kalman filtering with collapsed observation vector. 
filter_conventional  (bool) Flag for conventional Kalman filtering. 
filter_exact_initial  (bool) Flag for exact initial Kalman filtering. 
filter_extended  (bool) Flag for extended Kalman filtering. 
filter_method  (int) Filtering method bitmask. 
filter_methods  
filter_square_root  (bool) Flag for squareroot Kalman filtering. 
filter_timing  (int) Filter timing. 
filter_univariate  (bool) Flag for univariate filtering of multivariate observation vector. 
filter_unscented  (bool) Flag for unscented Kalman filtering. 
inversion_method  (int) Inversion method bitmask. 
inversion_methods  
invert_cholesky  (bool) Flag for Cholesky inversion method. 
invert_lu  (bool) Flag for LU inversion method. 
invert_univariate  (bool) Flag for univariate inversion method (recommended). 
memory_conserve  (bool) Flag to conserve the maximum amount of memory. 
memory_no_filtered  (bool) Flag to prevent storing filtered state and covariance matrices. 
memory_no_forecast  (bool) Flag to prevent storing forecasts. 
memory_no_gain  (bool) Flag to prevent storing the Kalman gain matrices. 
memory_no_likelihood  (bool) Flag to prevent storing likelihood values for each observation. 
memory_no_predicted  (bool) Flag to prevent storing predicted state and covariance matrices. 
memory_no_smoothing  (bool) Flag to prevent storing likelihood values for each observation. 
memory_no_std_forecast  (bool) Flag to prevent storing standardized forecast errors. 
memory_options  
memory_store_all  (bool) Flag for storing all intermediate results in memory (default). 
obs  (array) Observation vector – \(y~(k\_endog \times nobs)\) 
obs_cov  
obs_intercept  
prefix  (str) BLAS prefix of currently active representation matrices 
selection  
solve_cholesky  (bool) Flag for Cholesky and linear solver inversion method (recommended). 
solve_lu  (bool) Flag for LU and linear solver inversion method. 
stability_force_symmetry  (bool) Flag for enforcing covariance matrix symmetry 
stability_method  (int) Stability method bitmask. 
stability_methods  
state_cov  
state_intercept  
time_invariant  (bool) Whether or not currently active representation matrices are timeinvariant 
timing_init_filtered  (bool) Flag for the alternate timing convention (Kim and Nelson, 2012). 
timing_init_predicted  (bool) Flag for the default timing convention (Durbin and Koopman, 2012). 
timing_options  
transition 
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.tsa.statespace.kalman_filter.KalmanFilter.html