关键词: 时间序列, 子序列匹配, 动态时间归整, 数据流

Abstract: Recently，techniques for data stream have been applied in widespread fields such as financial analysis，network monitoring，and sensor network，etc.The existing techniques，solving the similarity matching，are mainly for time series databases.However，it is difficult to adapt to stream data directly due to the high speed，continuity，real time and large quantity.Therefore，subsequence matching over data stream becomes a meaningful and challenging problem in a progressive and real-time fashion.In this paper，a novel bound technique based on DTW has been designed to make the best of similarity threshold to prune the redundant computing，as well as is fit for data streams in a“single pass”.Experiments with synthetic and real data show that the proposed method is at least 3 times faster than existing algorithm：SPRING，and only increasing several bytes without the loss of precision.

Key words: time series, subsequence matching, Dynamic Time Warping（DTW）, data stream