A Comparison of Multivariate Time Series Clustering Methods

Abstract

Big Data and the IoT explosion has made clustering Multivariate Time Series (MTS) one of the most effervescent research fields. From Bio-informatics to Business and Management, MTS are becoming more and more interesting as they allow to match events the co-occur in time but that is hardly noticeable. In this paper, we compare four clustering methods retrieved from the literature analyzing their performance on five publicly available data sets. These methods make use of different TS representation and distance measurement functions. Results show that Dynamic Time Warping is still competitive; APCA+DTW and Compression-based dissimilarity obtained the best results on the different data sets.