Limited-length Suffix-array-based Method for Variable-length Motif Discovery in Time Series

In this paper, we explore two key problems in time series motif discovery: releasing the constraints of trivial matching between subsequences with different lengths and improving the time and space efficiency. The purpose of avoiding trivial matching is to avoid too much repetition between subsequences in calculating their similarities. We describe a limited-length enhanced suffix array based framework (LiSAM) to resolve the two problems. Experimental results on Electrocardiogram signals indicate the accuracy of LiSAM on finding motifs with different lengths.