TY - JOUR A1 - Jain, Brijnesh T1 - Making the dynamic time warping distance warping-invariant JF - Pattern Recognition N2 - 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—despite its name and contrary to its characterization in some publications. 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. KW - Nearest-neighbor rule KW - Dynamic time warping KW - Time series KW - Semi-metric Y1 - 2019 U6 - https://doi.org/10.1016/j.patcog.2019.05.016 VL - 94 IS - October SP - 35 EP - 52 PB - Elsevier ER -