基于加权动态时间弯曲的多元时间序列相似性匹配方法<sup>*</sup>

doi:10.16451/j.cnki.issn1003-6059.201704003

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Abstract In most of the current methods, the close correlation between variables and the shape characteristics of time series is neglected. In this paper, a similarity matching method for multivariate time series is proposed based on combined principal component analysis method and a shape-based improved weighted dynamic time warping algorithm(CPCA-SWDTW). Firstly, a shape coefficient is introduced and a shape based weighted dynamic time warping(SWDTW) algorithm is presented. Next, the principal components of multivariate time series are extracted as the representation, and thus the variable correlations can be eliminated. Besides, the variance devoting rate of each principal component is considered as the weight of each series. On the basis of the proposed representation, SWDTW is used to measure the similarity between transformed multivariate time series. Finally, the results of similarity search experiment show that CPCA-SWDTW is more efficient and robust. Moreover, the parameter sensitivity analysis experiment show that CPCA-SWDTW can be affected by the parameters in weight function to some extent.

Key words： Multivariate Time Series Similarity Matching Combined Principal Component Analysis Weighted Dynamic Time Warping

Received: 15 October 2016

ZTFLH:

TP 391

Fund:Supported by of National Natural Science Foundation of China(No.71671186,71571185,71501182)

About author:: YE Yanqing, born in 1990, Ph.D. candidate. Her research interests include pattern recognition, data mining and knowledge graph.)
(YANG Kewei, born in 1977, Ph.D., professor. His research interests include defense acquisition, requirement analysis and modeling and intelligent agent simulation.)
(JIANG Jiang, born in 1981, Ph.D., associate professor. His research interests include evidential reasoning, uncertainty decision-making and risk ana-
lysis.)
(GE Bingfeng, born in 1983, Ph.D., lecturer. His research interests include conflict resolution and multi-objective programming.)
(DOU Yajie, born in 1987, Ph.D., lecturer. His research interests include portfolio selection decision-making and multi-objective programming.)

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Cite this article:

YE Yanqing,YANG Kewei,JIANG Jiang等. Multivariate Time Series Similarity Matching Method Based on Weighted Dynamic Time Warping Algorithm[J]. , 2017, 30(4): 314-327.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201704003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2017/V30/I4/314

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