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.
叶燕清 ， 杨克巍， 姜江 ， 葛冰峰， 豆亚杰. 基于加权动态时间弯曲的多元时间序列相似性匹配方法*[J]. 模式识别与人工智能, 2017, 30(4): 314-327.
YE Yanqing, YANG Kewei, JIANG Jiang, GE Bingfeng, DOU Yajie. Multivariate Time Series Similarity Matching Method Based on Weighted Dynamic Time Warping Algorithm. , 2017, 30(4): 314-327.
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