Tracking recurring patterns in time series using dynamic time warping

Rik Van Der Vlist, Cees Taal, Richard Heusdens

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Dynamic time warping (DTW) is a distance measure to compare time series that exhibit similar patterns. In this paper, we will show how the warping path of the DTW algorithm can be interpreted, and a framework is proposed to extend the DTW algorithm. Using this framework, we will show how the dynamic programming structure of the DTW algorithm can be used to track repeating patterns in time series.

Original languageEnglish
Title of host publicationEUSIPCO 2019 - 27th European Signal Processing Conference
PublisherEuropean Signal Processing Conference, EUSIPCO
Number of pages5
Volume2019-September
ISBN (Electronic)9789082797039
DOIs
Publication statusPublished - 1 Sep 2019
Event27th European Signal Processing Conference, EUSIPCO 2019 - A Coruna, Spain
Duration: 2 Sep 20196 Sep 2019

Conference

Conference27th European Signal Processing Conference, EUSIPCO 2019
CountrySpain
CityA Coruna
Period2/09/196/09/19

Keywords

  • Dynamic programming
  • Dynamic time warping
  • Time series analysis

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  • Cite this

    Van Der Vlist, R., Taal, C., & Heusdens, R. (2019). Tracking recurring patterns in time series using dynamic time warping. In EUSIPCO 2019 - 27th European Signal Processing Conference (Vol. 2019-September). European Signal Processing Conference, EUSIPCO. https://doi.org/10.23919/EUSIPCO.2019.8903102