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Front. Inform. Technol. Electron. Eng.  2011, Vol. 12 Issue (12): 1000-1009    DOI: 10.1631/jzus.C1100037
    
Comprehensive and efficient discovery of time series motifs
Lian-hua Chi*,1, He-hua Chi2, Yu-cai Feng1, Shu-liang Wang3, Zhong-sheng Cao1
1 School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China 2 State Key Laboratory of Software Engineering, Computer School, Wuhan University, Wuhan 430079, China 3 International School of Software, Wuhan University, Wuhan 430079, China
Comprehensive and efficient discovery of time series motifs
Lian-hua Chi*,1, He-hua Chi2, Yu-cai Feng1, Shu-liang Wang3, Zhong-sheng Cao1
1 School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China 2 State Key Laboratory of Software Engineering, Computer School, Wuhan University, Wuhan 430079, China 3 International School of Software, Wuhan University, Wuhan 430079, China
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摘要： 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.
关键词： Time series motifs;  Definition of K-motifs;  Optimized matrix structure;  Fast pruning method    
Abstract: 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.
Key words: Time series motifs    Definition of K-motifs    Optimized matrix structure    Fast pruning method
收稿日期: 2011-02-16 出版日期: 2011-11-30
CLC:  TP391.4  
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Lian-hua Chi
He-hua Chi
Yu-cai Feng
Shu-liang Wang
Zhong-sheng Cao

引用本文:

Lian-hua Chi, He-hua Chi, Yu-cai Feng, Shu-liang Wang, Zhong-sheng Cao. Comprehensive and efficient discovery of time series motifs. Front. Inform. Technol. Electron. Eng., 2011, 12(12): 1000-1009.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C1100037    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2011/V12/I12/1000

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