KalmanFilter.filter()

statsmodels.tsa.statespace.kalman_filter.KalmanFilter.filter

KalmanFilter.filter(filter_method=None, inversion_method=None, stability_method=None, conserve_memory=None, tolerance=None, loglikelihood_burn=None, results=None, complex_step=False) [source]

Apply the Kalman filter to the statespace model.

Parameters:

filter_method : int, optional Determines which Kalman filter to use. Default is conventional. inversion_method : int, optional Determines which inversion technique to use. Default is by Cholesky decomposition. stability_method : int, optional Determines which numerical stability techniques to use. Default is to enforce symmetry of the predicted state covariance matrix. conserve_memory : int, optional Determines what output from the filter to store. Default is to store everything. tolerance : float, optional The tolerance at which the Kalman filter determines convergence to steady-state. Default is 1e-19. loglikelihood_burn : int, optional The number of initial periods during which the loglikelihood is not recorded. Default is 0. results : class, object, or {‘loglikelihood’}, optional If a class which is a subclass of FilterResults, then that class is instantiated and returned with the result of filtering. Classes must subclass FilterResults. If an object, then that object is updated with the new filtering results. If the string ‘loglikelihood’, then only the loglikelihood is returned as an ndarray. If None, then the default results object is updated with the result of filtering.