ShiftDTW: adapting the DTW metric for cyclic time series clustering
Abstract
The elasticity of the DTW metric provides a more flexible comparison between time series and is used in numerous machine learning domains such as classification or clustering. However, it does not align the measurements at the beginning and end of time series if they have a shift occurring right at the start of one series, with the omitted part appearing at the end of that series. Due to the cyclicity of such series - which lack a definite beginning or end - we rely on the Cyclic DTW approach to propose a less computationally expensive approximation of this calculation method. This approximation will then be employed in conjunction with the K-Means clustering method.
Keywords
Cite
@article{arxiv.2402.05631,
title = {ShiftDTW: adapting the DTW metric for cyclic time series clustering},
author = {Lucas Foulon and Ilyes Korichi and Xavier Millot},
journal= {arXiv preprint arXiv:2402.05631},
year = {2024}
}
Comments
in French language. GAST@EGC 2024 : Atelier Gestion et Analyse des donn{\'e}es Spatiales et Temporelles, Aur{\'e}lie Leborgne; Nida Meddouri; Lo{\"i}c Salmon; Cl{\'e}ment Iphar, Jan 2024, Dijon, France