Faster Sequential Search with a Two-Pass Dynamic-Time-Warping Lower Bound
Abstract
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 (LBKeogh). We compare LBKeogh with a tighter lower bound (LBImproved). We find that LBImproved-based search is faster for sequential search. As an example, our approach is 3 times faster over random-walk and shape time series. We also review some of the mathematical properties of the DTW. We derive a tight triangle inequality for the DTW. We show that the DTW becomes the l1 distance when time series are separated by a constant.
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