IMPROVING TIME SERIES CLASSIFICATION ACCURACY: COMBINING GLOBAL AND LOCAL INFORMATION IN THE SIMILARITY CRITERION

Abstract

Given the widespread use of time series classification in many domains, how to improve the accuracy of classification has attracted considerable focus. In this paper, a new similarity measure (SIMscl) based on the global and local information has been proposed for improving the precision rate of one nearest neighbor (1NN) classifier. Specifically, the global information records the intrinsic properties of time series, and is reflected by two indicators: the shape information and the complexity; the local information pays attention to the exact match of value, and is realized by LB_keogh. Simultaneously, a method based on multi-scale discrete haar wavelet transform, key point extraction, and symbolization has been put forward to extract the shape information. To test the efficacy of the proposed shape similarity SIMshape and hybrid similarity SIMscl, the experiments are conducted on two data sets: star light curve and beef. Experimental evaluations show that SIMshape can deal with some time series misclassified by Euclidean Distance (ED), LB_keogh, and Complexity Invariant Distance (CID), and SIMscl has higher precision than ED, LB_keogh, and CID in time series 1NN classification.