Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower Bound

Details Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower Bound Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower Bound

2008-11-20

arxiv.org/abs/0811.3301v2

Daniel Lemire, Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower Bound, Pattern Recognition 42(9): 2169-2180 (2009)

Computer Science

Databases

Accepted in Pattern Recognition on November 20th, 2008

Scientific paper

10.1016/j.patcog.2008.11.030

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.

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