一种基于信息熵和DTW的多维时间序列相似性度量算法

A similarity metric algorithm for multivariate time series based on information entropy and DTW

提出一种基于信息熵和动态时间规整(DTW)的多维时间序列相似性度量的方法。首先,基于马氏距离(mahalanobis distance)的DTW,不仅考虑了多维时间序列的各个变量间的相互关系,而且对于长度不同的时间序列,通过动态规整可以进行准确地对齐。其次,利用信息熵理论,通过最小化损失函数,对马氏距离矩阵进行学习,来获得全局最优的马氏矩阵。为了验证所提算法的效果,选用UCI数据集中的5个数据集,采用最近邻分类算法对其进行分类实验。实验结果表明:该算法相比于其他算法,具有较高的分类准确率,且时间消耗较少。

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.