Proposal of a New Similarity Measure Based on Delay Embedding for Time Series Classification

Abstract

Time series data is abundant in many areas of practical life such as medical and health related applications, biometric or process industry, financial or economical analysis etc. The categorization of multivariate time series (MTS) data poses problem due to its dynamical nature and conventional machine learning algorithms for static data become unsuitable for time series data processing. For classification or clustering, a similarity measure to assess similarity between two MTS data is needed. Though various similarity measures have been developed so far, dynamic time warping (DTW) and its variants have been found to be the most popular. An approach of time series classification with a similarity measure (Cross Translational Error CTE) based on multidimensional delay vector (MDV) representation of time series has been proposed previously. In this work another new similarity measure (Dynamic Translational Error DTE), an improved version of CTE, and its two variants are proposed and the performance study of DTE(1) and DTE(2) in comparison to several other currently available similarity measures have been done using 43 publicly available bench mark data sets with simulation experiments. It has been found that the new measures produce the best recognition accuracy in larger number of data sets compared to other measures.