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
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