Using derivatives in a longest common subsequence dissimilarity measure for time series classification

Abstract

Over recent years the popularity of time series has soared. Given the widespread use of modern information technology, a large number of time series may be collected. As a consequence there has been a dramatic increase in the amount of interest in querying and mining such data. A vital component in many types of time series analyses is the choice of an appropriate dissimilarity measure. Numerous measures have been proposed to date, with the most successful ones based on dynamic programming. One of such measures is longest common subsequence (LCSS). In this paper, we propose a parametrical extension of LCSS based on derivatives. In contrast to well-known measures from the literature, our approach considers the general shape of a time series rather than point-to-point function comparison. The new dissimilarity measure is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted a set of experiments, testing effectiveness on 47 real time series. Experiments show that our method provides a higher quality of classification compared with LCSS on examined data sets.