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Making the dynamic time warping distance warping-invariant

  • The literature postulates that the dynamic time warping (dtw) distance can cope with temporal variations but stores and processes time series in a form as if the dtw-distance cannot cope with such variations. To address this inconsistency, we first show that the dtw-distance is not warping-invariant—despite its name and contrary to its characterization in some publications. The lack of warping-invariance contributes to the inconsistency mentioned above and to a strange behavior. To eliminate these peculiarities, we convert the dtw-distance to a warping-invariant semi-metric, called time-warp-invariant (twi) distance. Empirical results suggest that the error rates of the twi and dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense. However, the twi-distance requires less storage and computation time than the dtw-distance for a broad range of problems. These results challenge the current practice of applying the dtw-distance in nearest-neighbor classification and suggest the proposed twi-distance as a more efficient and consistent option.

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Metadaten
Author:Brijnesh JainORCiD
DOI:https://doi.org/10.1016/j.patcog.2019.05.016
Parent Title (English):Pattern Recognition
Publisher:Elsevier
Document Type:Article
Language:English
Year of first Publication:2019
Release Date:2022/05/01
Tag:Dynamic time warping; Nearest-neighbor rule; Semi-metric; Time series
Volume:94
Issue:October
First Page:35
Last Page:52
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
Publication:Externe Publikationen
research focus:Digitalisierung
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG