Shapelet Classification Algorithm Based on Efficient Subsequence Matching

Abstract

Shapelet classification algorithms are an accurate classification method for time series data. Existing shapelet classifying processes are relatively inefficient and slow due to the large amount of necessary complex distance computations. This paper therefore introduces piecewise aggregate approximation(PAA) representation and an efficient subsequence matching algorithm for shapelet classification algorithms; the paper also proposes shapelet transformation classification algorithm based on efficient series matching. First, the proposed algorithm took the PAA representation for appropriate dimension reduction, and then used a subsequence matching algorithm to simplify the data classification process. The research experimented on 14 public time series datasets taken from UCI and UCR, used the original and new algorithm for classification, and compared the efficiency and accuracy of the two methods. Experimental results showed that the efficient subsequence matching algorithm could be combined with the shapelet classification algorithm; the new algorithm could ensure relatively high classification accuracy, effectively simplified the algorithm calculation process, and improved classification efficiency.