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Multivariate time series classification with parametric derivative dynamic time warping
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DOI：10.1016/j.eswa.2014.11.007.png)
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Multivariate time series (MIS) data are widely used in a very broad range of fields, including medicine, finance, multimedia and engineering. In this paper a new approach for MTS classification, using a parametric derivative dynamic time warping distance, is proposed. Our approach combines two distances: the DTW distance between MTS and the DTW distance between derivatives of MTS. The new distance is used in classification with the nearest neighbor rule. Experimental results performed on 18 data sets demonstrate the effectiveness of the proposed approach for MTS classification. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Dynamic time warping
Derivative dynamic time warping
Multivariate time series
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IF:
7.5
论文数:
3.0W
被引数:
10.2W
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