Hi Kyle,
personally, I'd love to see SAX in sklearn or any other python library that
I could easily use with sklearn. We don't have any time-series specific
functionality yet (eg. lagged features transformer). So if we choose to add
time-series functionality we should also consider the basics.
Lets hear what the others say about this.
PS: I'd not put it into decomposition but rather feature_extraction.tseries
or something along those lines.
best,
Peter
2013/9/25 Kyle Kastner <[email protected]>
> I have recently been working with time-series data extensively and looking
> at different ways to model, classify, and predict different types of
> time-series.
>
> One algorithm I have been playing with is called SAX (
> http://www.cs.ucr.edu/~eamonn/SAX.htm). It is a very straightforward
> algorithm (basically windowed mean with no overlap, then quantize into M
> levels), and I have implemented a rough version using numpy. Despite its
> simplicity, it is shown as being an effective data dependent transform,
> similar in some ways to the DWT.
>
> I think this algorithm would be a nice tie-in to sklearn, which could
> allow for more of sklearn's algorithms to be used on time-series type data.
> Also, the algorithm makes very strong claims about indexing massive
> datasets, finding similarities and outliers, which are all things I am
> planning to explore in the future.
>
> I know that FastICA is under decomposition, and is often seen in a
> time-series context - would symbolic aggregation fall into the
> "decomposition" camp as well? Is sklearn even the right place for this?
>
> Kyle
>
>
> ------------------------------------------------------------------------------
> October Webinars: Code for Performance
> Free Intel webinars can help you accelerate application performance.
> Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most
> from
> the latest Intel processors and coprocessors. See abstracts and register >
> http://pubads.g.doubleclick.net/gampad/clk?id=60133471&iu=/4140/ostg.clktrk
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
--
Peter Prettenhofer
------------------------------------------------------------------------------
October Webinars: Code for Performance
Free Intel webinars can help you accelerate application performance.
Explore tips for MPI, OpenMP, advanced profiling, and more. Get the most from
the latest Intel processors and coprocessors. See abstracts and register >
http://pubads.g.doubleclick.net/gampad/clk?id=60133471&iu=/4140/ostg.clktrk
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
