A Proposal for Cost Aware Edge-Detectional Dynamic Time Warping for Time Series Classification

Abstract

Dynamic Time Warping (DTW) is a well known algorithm for measuring similarity of two time series and widely used in classification, clustering or regression problems related to time series data. Unlike simple Euclid distance measure, DTW can handle time series of unequal lengths and is able to find an optimal alignment between two time sequences. Though very efficient, the computational cost of DTW is very high. There are several suboptimal variants of DTW for lowering computation, none of them is perfect. In this work, an approach to reduce computational burden of DTW has been proposed from the perspective of removing unimportant portion of the time series from computing, selected by a mask generated by edge detection algorithm commonly used in image processing or computer vision. The proposed Edge-Detectional Dynamic Time Warping (EDDTW) has been compared with original DTW by simulation experiments with 43 publicly available benchmark data sets. The simulation results show that EDDTW outperforms DTW regarding classification accuracy in more than half of the data sets, while reducing on the average 60% of the original time series leading to reduction in computational time.