基于分段特征及自适应加权的DTW相似性度量

利用动态时间弯曲(DTW)技术在原始多元时间序列进行相似性度量时时间复杂度较高,且DTW在追求最小弯曲距离的过程中可能会出现过渡拉伸和压缩的问题.提出一种基于分段特征及自适应加权的DTW多元时间序列相似性度量方法.对原始时间序列在各个变量维度上进行统一分段,选取分段后拟合线段的斜率、分段区间的最大值和最小值以及时间跨度...

DTW Similarity Measure Based on Segmentation Features and Adaptive Weighting

Dynamic Time Warping(DTW) faces high time complexity in the similarity measure for raw multivariate time series, and DTW sometimes causes transition stretching and compression in the process of pursuing minimum bending distance. To address the problem, this paper proposes a new DTW method based on segmentation features and adaptive weighting for similarity measurement of multivariate time series. The raw time series is segmented in each variable dimension. Then the certain segments are selected, and their gradient, maximum and minimum values, as well as the time span are taken as the features of each segment. So the dimensions of the raw series can be significantly reduced to improve the computational efficiency. During the calculation of the optimal bending path by DTW, the adaptive cost weight is set for each point to limit the number of reused points in the bending path, and effectively improve the low measurement accuracy caused by the over-stretching or compression of the time series. So the optimal path can be obtained. Experimental results show that the proposed method can measure the similarity between multiple time series and provide excellent measurement results on multiple datasets.