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
Clicked: 8723
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.
@article{title="Comprehensive and efficient discovery of time series motifs",
author="Lian-hua Chi, He-hua Chi, Yu-cai Feng, Shu-liang Wang, Zhong-sheng Cao",
journal="Journal of Zhejiang University Science C",
volume="12",
number="12",
pages="1000-1009",
year="2011",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.C1100037"
}
%0 Journal Article
%T Comprehensive and efficient discovery of time series motifs
%A Lian-hua Chi
%A He-hua Chi
%A Yu-cai Feng
%A Shu-liang Wang
%A Zhong-sheng Cao
%J Journal of Zhejiang University SCIENCE C
%V 12
%N 12
%P 1000-1009
%@ 1869-1951
%D 2011
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.C1100037
TY - JOUR
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
VL - 12
IS - 12
SP - 1000
EP - 1009
%@ 1869-1951
Y1 - 2011
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.C1100037
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.
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