[SciPy-Dev] STL / LOESS seasonal trend decomposition

[Skipper Seabold]

On Thu, May 10, 2012 at 5:52 AM, Andreas H. <lists at hilboll.de> wrote:
> Hi,
>
> googling for a Python implementation of STL (seasonal trend
> decomposition based on Loess, see
> http://cs.wellesley.edu/~cs315/Papers/stl%20statistical%20model.pdf)
> found this:
>
>
> http://projects.scipy.org/scipy/browser/trunk/scipy/sandbox/pyloess/mpyloess.py?rev=3473
>
> Can anyone tell me something about the status of this code? I'll
> probably need this for my work anyway, and thought it might be nice to
> bring it to a level at which it could be included into some package.
> Where would it belong? scipy.stats? statsmodels?
>

Statsmodels would definitely be interested in including something like
this. It doesn't look like the original implementation is BSD
compatible with SciPy/statsmodels though because of the "without fee"

http://projects.scipy.org/scipy/browser/trunk/scipy/sandbox/pyloess/LICENSE.txt?rev=3473

If you can code something up that doesn't rely on this, then by all
means. If it helps (I don't know the details of STL yet), we have a
LOWESS implementation here.

http://statsmodels.sourceforge.net/devel/generated/statsmodels.nonparametric.api.lowess.html#statsmodels.nonparametric.api.lowess

Any feedback on speed would be welcome, if you're comparing with the
above code. I've also started working on Seasonal ARIMA models in a
branch of my fork (jseabold) of statsmodels. I'd be interested to
compare STL to our other filtering methods used to decompose economic
time series into trend and cycle components

http://statsmodels.sourceforge.net/devel/tsa.html#other-time-series-filters

Skipper