# statsmodels.tsa.statespace.kalman_smoother.KalmanSmoother¶

class statsmodels.tsa.statespace.kalman_smoother.KalmanSmoother(k_endog, k_states, k_posdef=None, results_class=None, kalman_smoother_classes=None, **kwargs)[source]

State space representation of a time series process, with Kalman filter and smoother.

Parameters
k_endogarray_like or integer

The observed time-series process $$y$$ if array like or the number of variables in the process if an integer.

k_statesint

The dimension of the unobserved state process.

k_posdefint, optional

The dimension of a guaranteed positive definite covariance matrix describing the shocks in the measurement equation. Must be less than or equal to k_states. Default is k_states.

results_classclass, optional

Default results class to use to save filtering output. Default is SmootherResults. If specified, class must extend from SmootherResults.

**kwargs

Keyword arguments may be used to provide default values for state space matrices, for Kalman filtering options, or for Kalman smoothing options. See Representation for more details.

Attributes
design
dtype

(dtype) Datatype of currently active representation matrices

endog
obs

(array) Observation vector: $$y~(k\_endog \times nobs)$$

obs_cov
obs_intercept
prefix

(str) BLAS prefix of currently active representation matrices

selection
state_cov
state_intercept
time_invariant

(bool) Whether or not currently active representation matrices are

transition

Methods

 bind(endog) Bind data to the statespace representation filter([filter_method, inversion_method, …]) Apply the Kalman filter to the statespace model. fixed_scale(scale) Context manager for fixing the scale when FILTER_CONCENTRATED is set impulse_responses([steps, impulse, …]) Impulse response function initialize(initialization[, …]) Create an Initialization object if necessary initialize_approximate_diffuse([variance]) Initialize the statespace model with approximate diffuse values. Initialize the statespace model as stationary. initialize_known(constant, stationary_cov) Initialize the statespace model with known distribution for initial state. 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_smooth_method([smooth_method]) Set the smoothing method set_smoother_output([smoother_output]) Set the smoother output set_stability_method([stability_method]) Set the numerical stability method simulate(nsimulations[, measurement_shocks, …]) Simulate a new time series following the state space model smooth([smoother_output, smooth_method, …]) Apply the Kalman smoother to the statespace model.