Time Warp Edit Distance with Stiffness Adjustment for Time Series Matching

TL;DR

This paper introduces Time Warp Edit Distance (TWED), a novel metric for time series similarity that incorporates stiffness adjustment, enabling efficient retrieval and improved classification performance.

Contribution

The paper proposes TWED, a new elastic distance measure for time series that combines edit operations with a stiffness parameter, distinct from existing methods.

Findings

TWED is a metric with the triangle inequality property.

TWED outperforms existing methods in classification accuracy.

A lower bound relates downsampled and original time series matching.

Abstract

In a way similar to the string-to-string correction problem we address time series similarity in the light of a time-series-to-time-series-correction problem for which the similarity between two time series is measured as the minimum cost sequence of "edit operations" needed to transform one time series into another. To define the "edit operations" we use the paradigm of a graphical editing process and end up with a dynamic programming algorithm that we call Time Warp Edit Distance (TWED). TWED is slightly different in form from Dynamic Time Warping, Longest Common Subsequence or Edit Distance with Real Penalty algorithms. In particular, it highlights a parameter which drives a kind of stiffness of the elastic measure along the time axis. We show that the similarity provided by TWED is a metric potentially useful in time series retrieval applications since it could benefit from the triangular inequality property to speed up the retrieval process while tuning the parameters of the elastic measure. In that context, a lower bound is derived to relate the matching of time series into down sampled representation spaces to the matching into the original space. Empiric quality of the TWED distance is evaluated on a simple classification task. Compared to Edit Distance, Dynamic Time Warping, Longest Common Subsequnce and Edit Distance with Real Penalty, TWED has proven to be quite effective on the considered experimental task.