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- W2787542756 abstract "Detecting repeated variable-length patterns, also called variable-length motifs, has received a great amount of attention in recent years. Current state-of-the-art algorithm utilizes fixed-length motif discovery algorithm as a subroutine to enumerate variable-length motifs. As a result, it may take hours or days to execute when enumeration range is large. In this work, we introduce an approximate algorithm called HierarchIcal based Motif Enumeration (HIME) to detect variable-length motifs with a large enumeration range in million-scale time series. We show in the experiments that the scalability of the proposed algorithm is significantly better than that of the state-of-the-art algorithm. Moreover, the motif length range detected by HIME is considerably larger than previous sequence-matching based approximate variable-length motif discovery approach. We demonstrate that HIME can efficiently detect meaningful variable-length motifs in long, real world time series." @default.
- W2787542756 created "2018-02-23" @default.
- W2787542756 creator A5023590471 @default.
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- W2787542756 date "2018-02-13" @default.
- W2787542756 modified "2023-09-23" @default.
- W2787542756 title "Efficient Discovery of Variable-length Time Series Motifs with Large Length Range in Million Scale Time Series" @default.
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- W2787542756 doi "https://doi.org/10.48550/arxiv.1802.04883" @default.
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