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Time-series averaging using constrained dynamic time warping with tolerance.
- Source :
-
Pattern Recognition . Feb2018, Vol. 74, p77-89. 13p. - Publication Year :
- 2018
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00313203
- Volume :
- 74
- Database :
- Academic Search Index
- Journal :
- Pattern Recognition
- Publication Type :
- Academic Journal
- Accession number :
- 125923490
- Full Text :
- https://doi.org/10.1016/j.patcog.2017.08.015