Re: [math] A Java implementation of STL: A Seasonal-Trend Decomposition Procedure Based on Loess

[Greg Brandt]

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 
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
>> , 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
>
>

Re: [math] A Java implementation of STL: A Seasonal-Trend Decomposition Procedure Based on Loess

[Gilles]

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
, which we think might be 
generally

useful.

It is based on commons-math3 LoessInterpolator

,
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

[math] A Java implementation of STL: A Seasonal-Trend Decomposition Procedure Based on Loess

[Greg Brandt]

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
, which we think might be generally
useful.

It is based on commons-math3 LoessInterpolator
,
so we thought it might be a natural contribution to the project.

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.

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.

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.

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