An Effective Implementation of Motif-Based Time Series Classification

Abstract

Time series classification is one of the crucial tasks in time series data mining. Due to the unique characteristics of time series, most classic classification algorithms in data mining do not work well for time series. So far, empirical evidence has shown that the nearest neighbor method is very effective to classify directly time series data without any feature extraction step. Recently, there have been two research works that proposed a framework for time series classification which uses motif information to convert time series to feature vectors. This framework facilitates the use of classical classification algorithms for time series classification. In this paper, we present an effective implementation of the framework. In our method we exploit the motif discovery algorithm which combines EP-C, a segmentation-based motif discovery method and MK, an exact motif discovery method. Furthermore, we devise a segmentation-based method that helps in transforming efficiently a set of time series to a set of feature-vectors. Experimental results on benchmark datasets reveal that our proposed implementation method for motif-based time series classification improves the accuracy of not only 1-nearest neighbor algorithm but also SVMs and ANNs for time series classification.