Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6939722 | Pattern Recognition | 2018 | 27 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Marion Morel, Catherine Achard, Richard Kulpa, Séverine Dubuisson,