Time Series Classification Method Based on Longest Common Subsequence and Textual Approximation

Abstract

Many symbolic representations of time series have been proposed by researchers over past decades. However, it is still not enough to classify time series with high accuracy in such applications as ubiquitous systems or sensor systems. In this paper, we propose a new symbolic representation of time series called l-TAX to increase the accuracy of time series classification. A time series can be represented by term sequences in l-TAX. l-TAX is based on a document like symbolic representation of time series called TAX. We use longest common subsequence as our distance measure between textually approximated time series. During time series classification, consideration of symbol sequences increases the accuracy significantly. In our evaluation, we have demonstrated that l-TAX is effective for classification as well as searching time series data set.