# Similarity Learning for Time Series Classification

arXiv: https://arxiv.org/abs/1610.04783 · HTML: https://mlsanity.com/paper/1610.04783/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2016.

## Built on by (most cited first, 1 of 1)

- [Towards Similarity-Aware Time-Series Classification](https://mlsanity.com/paper/2201.01413/) (2022)

## Abstract (arXiv)

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.