Warping and Matching Subsequences Between Time Series

TL;DR

This paper introduces a novel visualization technique for time series comparison that simplifies warping paths to better highlight and interpret key subsequence transformations like shifts and compressions.

Contribution

It provides a new method to visualize and interpret subsequence alignments in time series, improving understanding beyond traditional point-to-point alignment visualizations.

Findings

Enhanced interpretability of time series alignments

Clear visualization of subsequence transformations

Improved understanding of structural relationships

Abstract

Comparing time series is essential in various tasks such as clustering and classification. While elastic distance measures that allow warping provide a robust quantitative comparison, a qualitative comparison on top of them is missing. Traditional visualizations focus on point-to-point alignment and do not convey the broader structural relationships at the level of subsequences. This limitation makes it difficult to understand how and where one time series shifts, speeds up or slows down with respect to another. To address this, we propose a novel technique that simplifies the warping path to highlight, quantify and visualize key transformations (shift, compression, difference in amplitude). By offering a clearer representation of how subsequences match between time series, our method enhances interpretability in time series comparison.