How do you plan to represent variable-length time series? Lists of 1d numpy
arrays work but would be slow I guess. The ideal representation needs to be
compatible with grid search and fast.
Mathieu
On Mon, Dec 7, 2015 at 10:35 AM, Dan Shiebler <danshieb...@gmail.com> wrote:
> Hello,
>
> I’m not sure if this is the correct place to send this. If it is not,
> could you please direct me to the best place? Thank you.
>
> I’d like to add a dynamic time warping metric to
> sklearn.neighbors.DistanceMetric.
> Dynamic time warping is one of the most used distance metrics for time
> series, and it would be very convenient for users if it were integrated
> into the module.
>
> Right now users can use an existing implementation of dynamic time warping
> as a custom metric for any of the nearest neighbors classes. However, this
> requires users to find a good existing implementation. In addition, users
> cannot take advantage of the LB Keogh lower bound of dynamic time warping,
> which can dramatically speed up the nearest neighbors search.
>
> I propose that first a “dtw” metric be added to the DistanceMetric class.
> After this integration is successful, I propose that the LB Keogh lower
> bound optimization be added to the NearestNeighbors class.
>
> Please let me know your thoughts on this, and I would happy to work on
> this if it would improve the scikit-learn module.
>
> Thank you,
> Dan
>
>
> ------------------------------------------------------------------------------
> Go from Idea to Many App Stores Faster with Intel(R) XDK
> Give your users amazing mobile app experiences with Intel(R) XDK.
> Use one codebase in this all-in-one HTML5 development environment.
> Design, debug & build mobile apps & 2D/3D high-impact games for multiple
> OSs.
> http://pubads.g.doubleclick.net/gampad/clk?id=254741911&iu=/4140
> _______________________________________________
> Scikit-learn-general mailing list
> Scikit-learn-general@lists.sourceforge.net
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
------------------------------------------------------------------------------
Go from Idea to Many App Stores Faster with Intel(R) XDK
Give your users amazing mobile app experiences with Intel(R) XDK.
Use one codebase in this all-in-one HTML5 development environment.
Design, debug & build mobile apps & 2D/3D high-impact games for multiple OSs.
http://pubads.g.doubleclick.net/gampad/clk?id=254741911&iu=/4140
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
