具有两遍动态时间弯曲下界的快速顺序搜索
Faster Sequential Search with a Two-Pass Dynamic-Time-Warping Lower Bound
摘要：
动态时间规整（DTW）是一种流行的时间序列相似性度量方法。DTW不满足三角不等式，其计算需要二次时间。因此，为了快速找到最近的邻居，我们使用边界技术。我们可以用一个便宜的下限（LB_Keogh）来避免大多数DTW计算。我们将LB_Keogh与更严格的下限（LB_Improved）进行比较。我们发现，LB_Improved为基础的搜索是更快的顺序搜索。例如，我们的方法比随机行走和形状时间序列快3倍。我们还回顾了DTW的一些数学性质。我们得到了DTW的一个紧三角不等式。我们证明了当时间序列被一个常数分开时，DTW变成l_1距离。
英文摘要：
The Dynamic Time Warping (DTW) is a popular similarity measure between time series. The DTW fails to satisfy the triangle inequality and its computation requires quadratic time. Hence, to find closest neighbors quickly, we use bounding techniques. We can avoid most DTW computations with an inexpensive lower bound (LB_Keogh). We compare LB_Keogh with a tighter lower bound (LB_Improved). We find that LB_Improved-based search is faster for sequential search. As an example, our approach is 3 times faster over random-walk and shape time series. We also review some of the mathematical properties of the DTW. We derive a tight triangle inequality for the DTW. We show that the DTW becomes the l_1 distance when time series are separated by a constant.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
