Computer Science ›› 2018, Vol. 45 ›› Issue (5): 180-184.doi: 10.11896/j.issn.1002-137X.2018.05.030

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Multivariate Time Series Classification Based on Shapelets Learning

ZHAO Hui-yun and PAN Zhi-song   

  • Online:2018-05-15 Published:2018-07-25

Abstract: Multivariate time series data exist in a wide range of real-life domains,and multivariate time series classification is a basic method of obtaining information from time series data.At present,time series classification is suffered from the problem that the similarity measure of time series data is special and the dimension of the original data is high,thus the classification performance of the existing multivariate time series classification methods still need to be improved.This paper presented a multivariate time series classification method based on shapelet learning.At first,this paper established a shapelets learning method under a regularized least squares loss learning framework,and the time series classification method with one dimension based on shapelets is used to classify the vrivariate data of multivariate time series.Then the final resut of the multivariate time series is determined through plurality voting.Experimental results indicate that the proposed method achieves high classification accuracy when processing multivariate time series classification problem.

Key words: Multivariate time series,Classification,Shapelets,Shapelets learning

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