This paper presents a method based on information entropy and dynamic time warping (DTW) to measure the similarity of multivariate time series. Firstly

DTW based on the Mahalanobis Distance considers the interrelationships among the variables of the multivariate time series

through the dynamic warping to align time series of different length. Secondly

adapting the information entropy theory

the Mahalanobis distance matrix is learned by minimizing the loss function

which can obtains the global optimal Markov matrix. In order to verify the effectiveness of the proposed algorithm

the five data sets in the UCI data set were used to classify through the nearest neighbor classification algorithm. Experimental results show that this method has higher classification accuracy and less time consumption than other methods

which proves the effectiveness of the proposed method.