Generalizing DTW to the multi-dimensional case requires an adaptive approach

Data Mining and Knowledge Discovery
Mohammad Shokoohi-YektaEamonn Keogh

Abstract

In recent years Dynamic Time Warping (DTW) has emerged as the distance measure of choice for virtually all time series data mining applications. For example, virtually all applications that process data from wearable devices use DTW as a core sub-routine. This is the result of significant progress in improving DTW's efficiency, together with multiple empirical studies showing that DTW-based classifiers at least equal (and generally surpass) the accuracy of all their rivals across dozens of datasets. Thus far, most of the research has considered only the one-dimensional case, with practitioners generalizing to the multi-dimensional case in one of two ways, dependent or independent warping. In general, it appears the community believes either that the two ways are equivalent, or that the choice is irrelevant. In this work, we show that this is not the case. The two most commonly used multi-dimensional DTW methods can produce different classifications, and neither one dominates over the other. This seems to suggest that one should learn the best method for a particular application. However, we will show that this is not necessary; a simple, principled rule can be used on a case-by-case basis to predict which of the two methods we ...Continue Reading

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Citations

Apr 2, 2018·The Journal of the Acoustical Society of America·Long N Le, Douglas L Jones
Nov 14, 2019·Scientific Reports·Matthew T StampsAjay S Mathuru
Sep 17, 2019·Journal of Exercise Rehabilitation·Hyun-Seob Lee
Mar 18, 2021·Data Mining and Knowledge Discovery·Chang Wei TanGeoffrey I Webb
May 15, 2021·Medical & Biological Engineering & Computing·Luis García-TerrizaJosé L Ayala
Oct 8, 2021·The American Journal of Clinical Nutrition·Luotao LinHeather A Eicher-Miller
Oct 17, 2021··Veronika SkvarlovaPetr Elias
Jun 18, 2018··Nenad MedvidovicArman Shahbazian

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