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

11/20/2008
by   Daniel Lemire, et al.
Association for Computing Machinery
0

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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Code Repositories

lbimproved

Dynamic Time Warping (DTW) library implementing lower bounds (LB_Keogh, LB_Improved...)


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dtw_lbimproved

Fast Nearest-Neighbor Retrieval under the Dynamic Time Warping


view repo