Set the smoothing method
The smoothing method can be used to override the Kalman smoother approach used. By default, the Kalman smoother used depends on the Kalman filter method.
Bitmask value to set the filter method to. See notes for details.
Keyword arguments may be used to influence the filter method by setting individual boolean flags. See notes for details.
The smoothing method is defined by a collection of boolean flags, and is internally stored as a bitmask. The methods available are:
- SMOOTH_CONVENTIONAL = 0x01
Default Kalman smoother, as presented in Durbin and Koopman, 2012 chapter 4.
- SMOOTH_CLASSICAL = 0x02
Classical Kalman smoother, as presented in Anderson and Moore, 1979 or Durbin and Koopman, 2012 chapter 4.6.1.
- SMOOTH_ALTERNATIVE = 0x04
Modified Bryson-Frazier Kalman smoother method; this is identical to the conventional method of Durbin and Koopman, 2012, except that an additional intermediate step is included.
- SMOOTH_UNIVARIATE = 0x08
Univariate Kalman smoother, as presented in Durbin and Koopman, 2012 chapter 6, except with modified Bryson-Frazier timing.
Practically speaking, these methods should all produce the same output but different computational implications, numerical stability implications, or internal timing assumptions.
Note that only the first method is available if using a Scipy version older than 0.16.
If the bitmask is set directly via the smooth_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 filter method may also be specified by directly modifying the class attributes which are defined similarly to the keyword arguments.
The default filtering method is SMOOTH_CONVENTIONAL.
>>> mod = sm.tsa.statespace.SARIMAX(range(10)) >>> mod.smooth_method 1 >>> mod.filter_conventional True >>> mod.filter_univariate = True >>> mod.smooth_method 17 >>> mod.set_smooth_method(filter_univariate=False, filter_collapsed=True) >>> mod.smooth_method 33 >>> mod.set_smooth_method(smooth_method=1) >>> mod.filter_conventional True >>> mod.filter_univariate False >>> mod.filter_collapsed False >>> mod.filter_univariate = True >>> mod.smooth_method 17