Hey Gilles, Thanks for the comments.
>> Would you be around to to keep maintaining to code? Yes. >> Out of curiousity, can it detect any kind of periodicity? The goal of the algorithm is to decompose a time series with known seasonality / periodicity into trend, seasonal, and remainder components. One could conceivably try many different parameters for seasonality, then use some objective function, probably involving residuals, to come up with the best value. But this would probably be considered an application of STL. >> There was a proposal to include "triple exponential smoothing", but is was deemed to be already an "application" rather than a general tool. At first sight, this code seems more in the line of CM. Am I right? The goal of STL is very similar to the Holt Winters triple exponential smoothing method. So it may be the case that this falls into the application category with respect to CM. There are a lot of tunable parameters, and good values depend on data, so it's not so pure from a mathematical standpoint. However it definitely fills some void, as this algorithm ships with R. If CM isn't the right place for it, would you know of a good home? Thanks, -Greg On Mon, Mar 21, 2016 at 3:53 AM, Gilles <gil...@harfang.homelinux.org> wrote: > Hello. > > On Thu, 17 Mar 2016 20:31:14 -0700, Greg Brandt wrote: > >> Hey Commons Math, >> >> As part of some work on anomaly detection in time series data, a couple >> colleagues and I have put together a Java implementation of STL >> <http://www.wessa.net/download/stl.pdf>, which we think might be >> generally >> useful. >> >> It is based on commons-math3 LoessInterpolator >> >> < >> http://commons.apache.org/proper/commons-math/apidocs/org/apache/commons/math3/analysis/interpolation/LoessInterpolator.html >> >, >> so we thought it might be a natural contribution to the project. >> > > Looks interesting (although I couldn't take the time to read the paper). > > >> The code currently lives here: https://github.com/brandtg/stl-java. From >> a >> usability perspective, I think it needs some cleanup work, and it needs >> more thorough testing. >> > > Would you be around to to keep maintaining to code? > > However, an earlier variant of the code has run in production with decent >> results on reasonably large time series, so it is not too far off. >> > > Out of curiousity, can it detect any kind of periodicity? > > Is this something that would be valuable to Commons Math? A cursory search >> of JIRA and the mailing lists didn't turn up with anything, so my >> apologies >> if this has been previously discussed. >> > > There was a proposal to include "triple exponential smoothing", but is was > deemed to be already an "application" rather than a general tool. > At first sight, this code seems more in the line of CM. Am I right? > > Other committers' opinions requested... > > > Best regards, > Gilles > > P.S. Please open a JIRA report ("Wish" type) so that we don't loose track > in the flood of emails... > > If so, I can massage the code to be consistent with the Developers Guide, >> simplify usage, and add more test coverage, then follow the >> recommendations >> there to create a patch. >> >> Please feel free to submit pull requests or issues on the Github repo in >> addition to discussion on this thread. >> >> Thanks, >> -Greg >> > > > --------------------------------------------------------------------- > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > >
