Similarity search of time series based on dynamic time warping
Due to the high computational time and space complexity of dynamic time warping (DTW),it can not satisfy the similarity search in large-scale time series stream.A DTW-based time series stream search method was proposed,which used global constraints and time series normalization methods to improve the accuracy of the search.To solve the problem of the high cost in data standardization in time series stream,the lower bounds that combined the time series normalization with the envelope update incrementally method was proposed.The upper and lower bounds of the query sequence were stored in double loop buffers to further improve the data reading and calculating speed.Experimental results show that the proposed method has the same accuracy compared with the traditional static time series search method,but its search speed is faster and DTW lower bound distance compactness is better.
time series streamsimilarity searchdynamic time warpinglower bounddata normalization
NETL
NSTL
万方数据
维普
