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HIME: discovering variable-length motifs in large-scale time series

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Abstract

Detecting repeated variable-length patterns, also called variable-length motifs, has received a great amount of attention in recent years. The state-of-the-art algorithm utilizes a 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 the 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.

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  1. George Mason University, 4400 University Dr., Fairfax, VA, 22030, USA

    Yifeng Gao & Jessica Lin

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  1. Yifeng Gao
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  2. Jessica Lin
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Correspondence to Yifeng Gao.

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Gao, Y., Lin, J. HIME: discovering variable-length motifs in large-scale time series. Knowl Inf Syst 61, 513–542 (2019). https://doi.org/10.1007/s10115-018-1279-6

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