I want to use a Savitzky-Golay filter to smooth some data. There is a right width to use based on the data that it is smoothing. A number of papers basically use "eyeball norm" on the parameters but that feels like voodoo.
How do I get a measure like AIC to tell me which implementation is the proper one to use?
When I think about AIC, I do so in terms of RSS, number parameters, and number of degrees of freedom. I can measure RSS for the smoothed data, but I don't know how to think about parameter count or number of degrees of freedom in this context. Hints or pointers would be appreciated. Clear explanation would be delightful.
I'm personally using either R, or LabVIEW, but the actual software package isn't important.
Work so far:
- This post says "k is 2 parameters, n is samples, use RSS" without providing references or derivation.
- This reference is about the 'PoMoS' library which claimes to use genetic algorithms and information criteria to find optimal polynomial structure of time-series.