An Efficient Method for Discovering Variable-length Motifs in Time Series based on Suffix Array
Nguyen Ngoc Phien, Nguyen Trong Nhan, Duong Tuan Anh · 2019
Repeated patterns in time series, also called motifs, are approximately repeated subsequences embedded within a longer time series data. There have been some popular and effective algorithms for discovering motifs in time series. Nevertheless, these algorithms still have some weaknesses such as users have to choose an appropriate value of the motif length or suffer high computational cost. In this paper, we propose an efficient method for discovering motifs based on suffix array. This method consists of transforming a time series into a symbolic string and then finding repeated substring in the symbolic string based on the suffix array. Besides, we also speed up the execution time of the method by applying multi-core parallelism. Experimental results reveal that our proposed method can discover variable-length motifs and perform very fast on large time series datasets while providing high accuracy.