  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.
