4.7 Article

Time-series averaging using constrained dynamic time warping with tolerance

期刊

PATTERN RECOGNITION
卷 74, 期 -, 页码 77-89

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2017.08.015

关键词

Time-series averaging; Dynamic time warping; Local constraints; Constrained DTW barycenter averaging; Time-series classification

资金

  1. ENS Paris-Saclay

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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. (C) 2017 Elsevier Ltd. All rights reserved.

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