Amercing: an intuitive and effective constraint for dynamic time warping

Matthieu Herrmann, Geoffrey I. Webb

Research output: Contribution to journalArticleResearchpeer-review

9 Citations (Scopus)

Abstract

Dynamic Time Warping (DTW) is a time series distance measure that allows non-linear alignments between series. Constraints on the alignments in the form of windows and weights have been introduced because unconstrained DTW is too permissive in its alignments. However, windowing introduces a crude step function, allowing unconstrained flexibility within the window, and none beyond it. While not entailing a step function, a multiplicative weight is relative to the distances between aligned points along a warped path, rather than being a direct function of the amount of warping that is introduced. In this paper, we introduce Amerced Dynamic Time Warping (ADTW), a new, intuitive, DTW variant that penalizes the act of warping by a fixed additive cost. Like windowing and weighting, ADTW constrains the amount of warping. However, it avoids both abrupt discontinuities in the amount of warping allowed and the limitations of a multiplicative penalty. We formally introduce ADTW, prove some of its properties, and discuss its parameterization. We show on a simple example how it can be parameterized to achieve an intuitive outcome, and demonstrate its usefulness on a standard time series classification benchmark. We provide a demonstration application in C++ Herrmann(2021)[1].

Original languageEnglish
Article number109333
Number of pages10
JournalPattern Recognition
Volume137
DOIs
Publication statusPublished - May 2023

Keywords

  • Dynamic time warping
  • Elastic distance
  • Time series

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