Article ID Journal Published Year Pages File Type
394635 Information Sciences 2011 22 Pages PDF
Abstract

Dynamic time warping (DTW) is a powerful technique in the time-series similarity search. However, its performance on large-scale data is unsatisfactory because of its high computational cost and the fact that it cannot be indexed directly. The lower bound technique for DTW is an effective solution to this problem. In this paper, we explain the existing lower-bound functions from a unified perspective and show that they are only special cases under our framework. We then propose a group of lower-bound functions for DTW and compare their performances through extensive experiments. The experimental results show that the new methods are better than the existing ones in most cases, and a theoretical explanation of the results is also given. We further implement an index structure based on the new lower-bound function. Experimental results demonstrate a similar conclusion.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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