Online Discovery of Top-k Similar Motifs in Time Series Data [chapter]

Hoang Thanh Lam, Ninh Dang Pham, Toon Calders
2011 Proceedings of the 2011 SIAM International Conference on Data Mining  
A motif is a pair of non-overlapping sequences with very similar shapes in a time series. We study the online topk most similar motif discovery problem. A special case of this problem corresponding to k = 1 was investigated in the literature by Mueen and Keogh [2]. We generalize the problem to any k and propose space-efficient algorithms for solving it. We show that our algorithms are optimal in term of space. In the particular case when k = 1, our algorithms achieve better performance both in
more » ... erms of space and time consumption than the algorithm of Mueen and Keogh. We demonstrate our results by both theoretical analysis and extensive experiments with both synthetic and real-life data. We also show possible application of the top-k similar motifs discovery problem.
doi:10.1137/1.9781611972818.86 dblp:conf/sdm/LamCP11 fatcat:5xxoszjqfndjxfvmov6deqvu4u