statsmodels.tsa.statespace.simulation_smoother.SimulationSmoother.set_stability_method

SimulationSmoother.set_stability_method(stability_method=None, **kwargs)

Set the numerical stability method

The Kalman filter is a recursive algorithm that may in some cases suffer issues with numerical stability. The stability method controls what, if any, measures are taken to promote stability.

Parameters:
stability_methodint, optional

Bitmask value to set the stability method to. See notes for details.

**kwargs

Keyword arguments may be used to influence the stability method by setting individual boolean flags. See notes for details.

Notes

The stability method is defined by a collection of boolean flags, and is internally stored as a bitmask. The methods available are:

STABILITY_FORCE_SYMMETRY = 0x01

If this flag is set, symmetry of the predicted state covariance matrix is enforced at each iteration of the filter, where each element is set to the average of the corresponding elements in the upper and lower triangle.

If the bitmask is set directly via the stability_method argument, then the full method must be provided.

If keyword arguments are used to set individual boolean flags, then the lowercase of the method must be used as an argument name, and the value is the desired value of the boolean flag (True or False).

Note that the stability method may also be specified by directly modifying the class attributes which are defined similarly to the keyword arguments.

The default stability method is STABILITY_FORCE_SYMMETRY

Examples

>>> mod = sm.tsa.statespace.SARIMAX(range(10))
>>> mod.ssm.stability_method
1
>>> mod.ssm.stability_force_symmetry
True
>>> mod.ssm.stability_force_symmetry = False
>>> mod.ssm.stability_method
0

Last update: Feb 28, 2024