A Comprehensive Comparison of Distance Measures for Time Series Classification

Abstract

In the past two decades, interest in the area of time series has soared and many distance measures for time series have been proposed. The problem of pairwise similarity of time series is based on the underlying distance measure (which is not necessarily metric or even dissimilarity measure) and is common in many time series areas. To the best of our knowledge, there are over 40 distance measures already proposed in the literature. Thus, there is a need to decide which measure will be the most appropriate for our specific problem. The aim of our study is to give a comprehensive comparison of distance measures for time series classification enriched with extensive statistical analysis. We will follow a methodology that assumes evaluating the efficacy of distance measures by the prism of accuracy of 1NN classifier. Experimental results carried out on benchmark datasets originated from UCR Time Series Classification Archive are provided. We show that none of the distance measures is the best for all datasets, however there is a group performing statistically significantly better than the others.