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An average-compress algorithm for the sample mean problem under dynamic time warping

  • Computing a sample mean of time series under dynamic time warping is NP-hard. Consequently, there is an ongoing research effort to devise efficient heuristics. The majority of heuristics have been developed for the constrained sample mean problem that assumes a solution of predefined length. In contrast, research on the unconstrained sample mean problem is underdeveloped. In this article, we propose a generic average-compress (AC) algorithm to address the unconstrained problem. The algorithm alternates between averaging (A-step) and compression (C-step). The A-step takes an initial guess as input and returns an approximation of a sample mean. Then the C-step reduces the length of the approximate solution. The compressed approximation serves as initial guess of the A-step in the next iteration. The purpose of the C-step is to direct the algorithm to more promising solutions of shorter length. The proposed algorithm is generic in the sense that any averaging and any compression method can be used. Experimental results show that the AC algorithm substantially outperforms current state-of-the-art algorithms for time series averaging.

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Metadaten
Author:Brijnesh JainORCiD, Vincent Froese, David Schultz
DOI:https://doi.org/10.1007/s10898-023-01294-9
Parent Title (English):Journal of Global Optimization
Publisher:Springer
Document Type:Article
Language:English
Year of first Publication:2023
Release Date:2023/06/22
Tag:Fréchet function; Heuristic; Nonconvex optimization; Time series averaging
Institutes:Fakultät Informatik und Mathematik
Begutachtungsstatus:peer-reviewed
OpenAccess Publikationsweg:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Digitalisierung
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International