Time series classification based on multi-codebook important time subsequence approximation algorithm

Abstract

This paper proposes a multi-codebook important time subsequence approximation (MCITSA) algorithm for time series classification. MCITSA generates a codebook using important time subsequences for each class based on the difference of categories. In this way, each codebook contains the class information itself. To predict the class label of an unseen time series, MCITSA needs to compare the similarities between important time subsequences extracted from the unseen time series and codewords of each class. Experimental results on time series datasets demonstrate that MCITSA is more powerful than PVQA in classifying time series.