I had not thought about use inside a Pipeline - though now that you mention
it, that seems like the ideal use case for an algorithm like this. Is this
the PR you mentioned?
https://github.com/scikit-learn/scikit-learn/pull/1454
As far as lagged features transformer - are we talking about rolling
statistics? Something similar to pandas rolling_mean, rolling_apply, etc.?
I have poorly reimplemented that using ```stride_tricks``` more times than
I probably should have...
I will work up a gist for SAX in the next few days, and post it here. There
is a nice demo of turning time-series into bitmaps which I rather like. If
I linked the right issue above, I will try to hop in there and catch up on
the changes. Resampling in the pipeline also opens the door for very
interesting things from a time-series perspective...
Kyle
On Thu, Sep 26, 2013 at 6:10 AM, Olivier Grisel <[email protected]>wrote:
> 2013/9/25 Peter Prettenhofer <[email protected]>:
> > 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.
>
> I would start by implementing lagged features transformer as gist or
> as an example script to experiment how it would (or not) fit with the
> current scikit-learn API.
>
> We might have a problem though: the current Pipeline tool does not
> support changing the number of samples in a data which would probably
> be required for TS forecasting stuff. We have a similar issue for
> resampling transformers (for instance for dealing with class
> imbalance).
>
> We should probably make the Pipeline more flexible first to be able to
> properly address TS tasks.
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
>
> ------------------------------------------------------------------------------
> 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
>
------------------------------------------------------------------------------
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
