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CLC number: TP391.4

On-line Access: 2011-11-30

Received: 2011-02-16

Revision Accepted: 2011-06-15

Crosschecked: 2011-11-04

Cited: 3

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE C 2011 Vol.12 No.12 P.1000-1009


Comprehensive and efficient discovery of time series motifs

Author(s):  Lian-hua Chi, He-hua Chi, Yu-cai Feng, Shu-liang Wang, Zhong-sheng Cao

Affiliation(s):  School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China, State Key Laboratory of Software Engineering, Computer School, Wuhan University, Wuhan 430079, China, International School of Software, Wuhan University, Wuhan 430079, China

Corresponding email(s):   lianhua1221@gmail.com

Key Words:  Time series motifs, Definition of K-motifs, Optimized matrix structure, Fast pruning method

Lian-hua Chi, He-hua Chi, Yu-cai Feng, Shu-liang Wang, Zhong-sheng Cao. Comprehensive and efficient discovery of time series motifs[J]. Journal of Zhejiang University Science C, 2011, 12(12): 1000-1009.

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%A He-hua Chi
%A Yu-cai Feng
%A Shu-liang Wang
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%DOI 10.1631/jzus.C1100037

T1 - Comprehensive and efficient discovery of time series motifs
A1 - Lian-hua Chi
A1 - He-hua Chi
A1 - Yu-cai Feng
A1 - Shu-liang Wang
A1 - Zhong-sheng Cao
J0 - Journal of Zhejiang University Science C
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.C1100037

time series motifs are previously unknown, frequently occurring patterns in time series or approximately repeated subsequences that are very similar to each other. There are two issues in time series motifs discovery, the deficiency of the definition of K-motifs given by Lin et al. (2002) and the large computation time for extracting motifs. In this paper, we propose a relatively comprehensive definition of K-motifs to obtain more valuable motifs. To minimize the computation time as much as possible, we extend the triangular inequality pruning method to avoid unnecessary operations and calculations, and propose an optimized matrix structure to produce the candidate motifs almost immediately. Results of two experiments on three time series datasets show that our motifs discovery algorithm is feasible and efficient.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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