Affiliations: [a] Center for OPTical IMagery Analysis and Learning, Northwestern Polytechnical University, Xi’an, China | [b] Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, China
| [c] School of Business Management, Huaqiao University, Quanzhou, China
Abstract: Dynamic time warping has attracted wide attention in various fields for its high matching accuracy. In time series data mining, dynamic time warping is a robust similarity measure of multivariate time series. However, the high computational cost of dynamic time warping restricts its applications in large scale data sets. In this paper, we propose a novel approach to speed up dynamic time warping of multivariate time series. Multivariate time series are fitted with multidimensional piecewise lines; and then, important points are extracted as features to reduce the dimensions of multivariate time series; finally, the features are imported to dynamic time warping to measure the similarity of multivariate time series. Extensive empirical results indicate that the proposed method can effectively improve the efficiency of dynamic time warping for multivariate time series, and obtain satisfactory matching accuracy.
Keywords: Multivariate time series, dynamic time warping, computational complexity, speed up
