Time-series averaging using constrained dynamic time warping with tolerance. (February 2018)

• Record Type: Journal Article
• Authors: Morel, Marion; Achard, Catherine; Kulpa, Richard; Dubuisson, Séverine
• Is Part Of: Pattern recognition. Volume 74(2018:Feb.)
• Page Start: 77
• Page End: 89
• Publication Date: 2018-02
• Subjects: Time-series averaging -- Dynamic time warping -- Local constraints -- Constrained DTW barycenter averaging -- Time-series classification; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2017.08.015
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: Averaging of time-series of different lengths. Introducing constraints and tolerance to DBA (DTW Barycenter Averaging). Application on a time-series classification task. Abstract: In this paper, we propose an innovative averaging of a set of time-series based on the Dynamic Time Warping (DTW). The DTW is widely used in data mining since it provides not only a similarity measure, but also a temporal alignment of time-series. However, its use is often restricted to the case of a pair of signals. In this paper, we propose to extend its application to a set of signals by providing an average time-series that opens a wide range of applications in data mining process. Starting with an existing well-established method called DBA (for DTW Barycenter Averaging), this paper points out its limitations and suggests an alternative based on a Constrained Dynamic Time Warping. Secondly, an innovative tolerance is added to take into account the admissible variability around the average signal. This new modeling of time-series is evaluated on a classification task applied on several datasets and results show that it outperforms state of the art methods.