Myth 1: The ability of DTW to handle sequences of different lengths is a great advantage, and therefore the simple lower bound that requires different-length sequences to be reinterpolated to equal length is of limited utility . In fact, as we will show, there is no evidence in the literature to suggest this, and extensive empirical evidence presented here suggests that comparing sequences of different lengths and reinterpolating them to equal length produce no statistically significant difference in accuracy or precision/recall.
Myth 2: Constraining the warping paths is a necessary evil that we inherited from the speech processing community to make DTW tractable, and that we should find ways to speed up DTW with no (or larger) constraints. In fact, the opposite is true. As we will show, the 10% constraint on warping inherited blindly from the speech processing community is actually too large for real world data mining.
Myth 3: There is a need (and room) for improvements in the speed of DTW for data mining applications. In fact, as we will show here, if we use a simple lower bounding technique, DTW is essentially O(n) for data mining applications. At least for CPU time, we are almost certainly at the asymptotic limit for speeding up DTW.