On-line and dynamic time warping for time series data mining

Abstract

Dynamic time warping (DTW) is a robust method used to measure similarity of time series. To speed up the calculation of DTW, an on-line and dynamic time warping is proposed to the field of time series data mining. A sliding window is used to segment a long time series into several short subsequences, and an efficient DTW proposed to measure the similarity of each pair of short subsequences. Meanwhile, a forward factor is proposed to set an overlap warping path for the two adjacent subsequences, which makes the last warping path be close to the best warping path between two time series. The results of numerical experiments demonstrate that, in contrast to DTW, the proposed approach comparing to DTW measures the similarity of time series fast and validly, which improves the performance of the algorithm applied to the field of time series data mining.