A Novel Dynamic Time Wrapping Similarity Algorithm Optimized by Multi-Granularity

Dynamic time warping algorithm (DTW) is a method of measuring the similarity of time series.Concerning the problem that DTW cannot keep high classification accuracy when the computation speed improved, a FG-DTW method based on the idea of naive granular computing is proposed.In this method, firstly, better temporal granularity is acquired by calculating temporal variance feature and it is used to replace original time series; Secondly, the elastic size of under comparing time series granularity allow dynamic adjustment through DTW algorithm and optimal time series corresponding granularity is obtained; Finally, DTW distance is calculated by optimal corresponding granularity model.At the same time, the early termination strategy of infimum function is introduced to improve the efficiency of FG-DTW algorithm.Experiments show that the proposed algorithm improves the running rate and accuracy effectively.