Abstract
In the time series classification (TSC) problem, the calculation of the distance of two time series is the kernel issue. One of the famous methods for the distance calculation is the dynamic time warping (DTW) with \(O(n^2)\) time complexity, based on the dynamic programming. It takes very long time when the data size is large. In order to overcome the time consuming problem, the dynamic time warping with window (DTWW) combines the warping window into DTW calculation. This method reduces the computation time by restricting the number of possible solutions, so the answer of DTWW may not be the optimal solution. In this paper, we propose the minimum-first DTW method (MDTW) that expands the possible solutions in the minimum first order. Our method not only reduces the required computation time, but also gets the optimal answer.
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