Classification of time series using combination of DTW and LCSS dissimilarity measures
Tomasz Górecki · Communications in Statistics - Simulation and Computation · 2017
In the domain of time series, different dissimilarity measures are applied for comparing sequences, the most successful ones being based on dynamic programming. Such measures include Longest Common SubSequence () and Dynamic Time Warping (DTW). In this article, a novel method is proposed to measure the dissimilarity of time series. We propose a parametric combination of and derivative DTW. The new dissimilarity measure is used in classification with the 1NN rule. We empirically compare our new approach to and DTW used separately and demonstrate its superiority in classification accuracy. In addition, our method is statistically comparable with method proposed by Lines and Bagnall considered so far the most accurate method of time series classification.