A proposal for classification of multisensor time series data based on time delay embedding

Abstract

Multisensor time series data is common in many ap-plications of process industry, medical and health care, biometrics etc. Analysis of multisensor time series data requires analysis of multidimensional time series(MTS) which is challenging as they constitute a huge volume of data of dynamic nature. Traditional machine learning algorithms for classification and clustering developed for static data can not be applied directly to MTS data. Various techniques have been developed to represent MTS data in a suitable manner for analysis by popular machine learning algorithms. Though a plethora of different approaches have been developed so far, 1NN classifier based on dynamic time warping (DTW) has been found to be the most popular due to its simplicity. In this work, an approach for time series classification is proposed based on multidimensional delay vector representation of time series. Multivariate time series is considered here as a group of single time series and each time series is processed separately to be represented by a multidimensional delay vector (MDV). A simple simulation experiment with online handwritten signature data has been done with a similarity measure based on the MDV representation and classification performance is compared with DTW based classifier. The simulation results show that classification accuracy of the proposed approach is satisfactory while computational cost is lower than DTW method.