Abstract: An efficient lower bounding technique is proposed based on Dynamic Time Warping（DTW） for time series similarity search，which measures the distance between original sequence reduced dimensionality by Piecewise Aggregate Approximation（PAA） approximation method and query sequence reduced dimensionality by Grid Minimum Bounding Rectangle（GMBR） representation approach.Experimental results show that，comparing with related techniques past，the proposed technique yields bigger lower bounding distance，more tightness of bound，stronger power pruning ability and shorter run time，in favor of time series data mining.

Key words: time series, Dynamic Time Warping（DTW）, lower bounding, Grid Minimum Bounding Rectangle（GMBR）

摘要： 针对时间序列数据，提出一种新的基于动态时间弯曲的下界技术，该技术首先基于分段聚集近似的线性表示对原始序列进行降维，同时生成查询序列的网格最小边界矩形近似表示，然后利用基于动态时间弯曲距离对两者下界距离度量。实验结果表明，该下界技术与以往相关技术相比，能够产生更大的下界距离，具有更强的紧凑度、裁剪搜索空间能力以及更短的运行时间，有利于时间序列数据挖掘。

关键词: 时间序列, 动态时间弯曲, 下界, 网格最小边界矩形