ShapeDBA: Generating effective time series prototypes using ShapeDTW Barycenter Averaging

Ali Ismail-Fawaz, Hassan Ismail Fawaz, François Petitjean, Maxime Devanne, Jonathan Weber, Stefano Berretti, Geoffrey I. Webb, Germain Forestier

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Time series data can be found in almost every domain, ranging from the medical field to manufacturing and wireless communication. Generating realistic and useful exemplars and prototypes is a fundamental data analysis task. In this paper, we investigate a novel approach to generating realistic and useful exemplars and prototypes for time series data. Our approach uses a new form of time series average, the ShapeDTW Barycentric Average. We therefore turn our attention to accurately generating time series prototypes with a novel approach. The existing time series prototyping approaches rely on the Dynamic Time Warping (DTW) similarity measure such as DTW Barycentering Average (DBA) and SoftDBA. These last approaches suffer from a common problem of generating out-of-distribution artifacts in their prototypes. This is mostly caused by the DTW variant used and its incapability of detecting neighborhood similarities, instead it detects absolute similarities. Our proposed method, ShapeDBA, uses the ShapeDTW variant of DTW, that overcomes this issue. We chose time series clustering, a popular form of time series analysis to evaluate the outcome of ShapeDBA compared to the other prototyping approaches. Coupled with the k-means clustering algorithm, and evaluated on a total of 123 datasets from the UCR archive, our proposed averaging approach is able to achieve new state-of-the-art results in terms of Adjusted Rand Index.

Original languageEnglish
Title of host publication8th ECML PKDD Workshop, AALTD 2023 Turin, Italy, September 18–22, 2023 Revised Selected Papers
EditorsGeorgiana Ifrim, Romain Tavenard, Anthony Bagnall, Patrick Schaefer, Simon Malinowski, Thomas Guyet, Vincent Lemaire
Place of PublicationCham Switzerland
PublisherSpringer
Pages127-142
Number of pages16
ISBN (Electronic)9783031498961
ISBN (Print)9783031498954
DOIs
Publication statusPublished - 2023
EventEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases: ECML PKDD 2023 - Turin, Italy
Duration: 18 Sept 202322 Sept 2023
Conference number: 8th
https://link.springer.com/book/10.1007/978-3-031-49896-1 (Proceedings)
https://2023.ecmlpkdd.org/submissions/key-dates-deadlines/ (Website)

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume14343
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceEuropean Conference on Machine Learning European Conference on Principles and Practice of Knowledge Discovery in Databases: ECML PKDD 2023
Abbreviated titleECML PKDD 2023
Country/TerritoryItaly
CityTurin
Period18/09/2322/09/23
Internet address

Keywords

  • Clustering
  • Dynamic Time Warping
  • ShapeDTW
  • Time Series
  • Time Series Averaging

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