基于两遍动态时间弯曲下界的快速检索
Faster Retrieval 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。我们可以避免大多数DTW计算与廉价的下限（LB基奥）。我们比较LB Keogh与更严格的下限（LB改进）。我们发现基于LB改进的搜索更快。例如，我们的方法比随机行走和形状时间序列快2-3倍。
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. As an example, our approach is 2-3 times faster over random-walk and shape time series.
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。
