基于动态时间弯曲技术的流数据处理方法

提出基于动态时间弯曲技术的流数据处理方法，将一段时间内采集到的流数据作为一个时间序列来进行处理．由于同一时间段内数据流变化的影响因素基本相同，导致一些数据流变化存在错位相似，具体表现为数据流形状大致相同，但在时间上有所超前或延迟．对于这种错位相似的数据流采用常用的欧几里得测度法是无法识别的，而采用动态时间弯曲技术却可以很好地判断数据流的这种相似性．在采用动态时间弯曲路径法得到两个时间序列对应点的基础上提出了用预测法估计两个时间序列的关系，从而确定时间序列最佳匹配点的算法．

Data Stream Process Method Based on Dynamic Time Warping Technology

This paper proposed data stream process method based on dynamic time warping technology. The streaming data collected in a certain period is processed as a time series. Since in the same period the factors causing data streams changes are approximately the same, so there exists dislocation similarity among data streams waves. It behaves as, the data streams waves are similar to each other in shape, but forward or backward in time. Usual Euclidean distance measure method can not identify the similarity of dislocation data streams, but dynamic time warping technology can do well. Based on the dynamic time warping path method to calculate corresponding points of two time series, an algorithm using prediction to estimate relationship of two time series and then find out their best match point is proposed in this paper