  The literature postulates that the dynamic time warping (dtw) distance can
cope with temporal variations but stores and processes time series in a form as
if the dtw-distance cannot cope with such variations. To address this
inconsistency, we first show that the dtw-distance is not warping-invariant.
The lack of warping-invariance contributes to the inconsistency mentioned above
and to a strange behavior. To eliminate these peculiarities, we convert the
dtw-distance to a warping-invariant semi-metric, called time-warp-invariant
(twi) distance. Empirical results suggest that the error rates of the twi and
dtw nearest-neighbor classifier are practically equivalent in a Bayesian sense.
However, the twi-distance requires less storage and computation time than the
dtw-distance for a broad range of problems. These results challenge the current
practice of applying the dtw-distance in nearest-neighbor classification and
suggest the proposed twi-distance as a more efficient and consistent option.
