  Multivariate time series naturally exist in many fields, like energy,
bioinformatics, signal processing, and finance. Most of these applications need
to be able to compare these structured data. In this context, dynamic time
warping (DTW) is probably the most common comparison measure. However, not much
research effort has been put into improving it by learning. In this paper, we
propose a novel method for learning similarities based on DTW, in order to
improve time series classification. Making use of the uniform stability
framework, we provide the first theoretical guarantees in the form of a
generalization bound for linear classification. The experimental study shows
that the proposed approach is efficient, while yielding sparse classifiers.
