Time series clustering via matrix profile and community detection

Abstract

Time series clustering has been used in diverse scientific areas to extract valuable information from complex and massive time series datasets. To improve the quality and efficiency of the clustering method applied to the field of time series data mining, a method for time series clustering via matrix profile and social network techniques (TCMS) is proposed. Firstly, a matrix profile is utilised to quickly find one pair of the most similar subsequences derived from two time series. The degree of correlation between the two time series is measured as the number of the most similar subsequences. Then, a network is constructed which treats each time series as a vertex and regards the correlations between the time series as edges. The edge weights are assigned according to the degree of correlation. Finally, the network can be divided by a community detection method to achieve time series clustering. The experiments on 45 UCR datasets demonstrate that the proposed method is efficient. The comparison experiments with Multilevel-ɛNN, k-medoids, k-shape, RWS and fastKARs demonstrate that the proposed method is a better approach to clustering time series than state-of-the-art methods.