Discovering time series motifs based on multidimensional index and early abandoning

Time series motifs are pairs of previously unknown sequences in a time series database or subsequences of a longer time series which are very similar to each other. Since their formalization in 2002, discovering motifs has been used to solve problems in several application areas. In this paper, we propose a novel approach for discovering approximate motifs in time series. This approach is based on R*-tree and the idea of early abandoning. Our method is time and space efficient because it only saves Minimum Bounding Rectangles (MBR) of data in memory and needs a single scan over the entire time series database and a few times to read the original disk data in order to validate the results. The experimental results showed that our proposed algorithm outperforms the popular method, Random Projection, in efficiency.

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