Faster retrieval with a two-pass dynamic-time-warping lower bound

被引：112

作者：

Lemire, Daniel ^{[1
]}

机构：

[1] Univ Quebec, LICEF, Montreal, PQ H2X 3P2, Canada

来源：

PATTERN RECOGNITION | 2009年 / 42卷 / 09期

基金：

加拿大自然科学与工程研究理事会;

关键词：

Time series; Very large databases; Indexing; Classification; SEARCH;

D O I：

10.1016/j.patcog.2008.11.030

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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. (C) 2008 Elsevier Ltd. All rights reserved.

引用

页码：2169 / 2180

页数：12

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