基于小波和动态时间弯曲的时间序列相似匹配

Time series similar pattern matching based on wavelet and dynamic time warping

  • 摘要: 提出了一种基于小波和动态时间弯曲(DTW)距离的时间序列索引和相似匹配方法.该方法采用小波变换进行数据降维,利用R*-tree建立多维索引结构.给出了查询序列的DTW距离边界和其在小波空间的查询超矩形的计算方法,从而将原始空间的基于DTW距离的相似匹配转换为小波空间基于欧氏距离的相似匹配.证明了此匹配方法不会产生漏报,给出了基于DTW距离的范围查询算法和近邻查询算法.实验结果表明该方法具有较高匹配精度和其较低的计算代价.

     

    Abstract: The paper proposed a dynamic time warping (DTW) indexing and similar matching method of time series based on discrete wavelet transform, which reduced the dimensionality of time series by discrete wavelet transform and constructed multi-dimensional index structure by R*-tree. The DTW lower bound and its discrete wavelet transform of query sequence were computed to form a query super-rectangle, thus the similar matching in original space based on DTW was converted to that in wavelet transform space based on Euclidian distance. It was proved that the method guaranteed no false dismissals and proposed the range query algorithm and nearest neighbor query algorithm. The result showed that it was a higher query precision and lower computing cost.

     

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