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On-line Access: 2010-12-09

Received: 2010-10-28

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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.12 P.959-965


Biclustering of ARMA time series

Author(s):  Jeonghwa Lee, Chi-Hyuck Jun

Affiliation(s):  Department of Industrial and Management Engineering, Pohang University of Science and Technology, Pohang, Gyungbuk, Korea

Corresponding email(s):   bls83@postech.ac.kr, chjun@postech.ac.kr

Key Words:  Biclustering, Time series, Autoregressive moving average (ARMA), Maximum likelihood estimation (MLE)

Jeonghwa Lee, Chi-Hyuck Jun. Biclustering of ARMA time series[J]. Journal of Zhejiang University Science A, 2010, 11(12): 959-965.

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author="Jeonghwa Lee, Chi-Hyuck Jun",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%T Biclustering of ARMA time series
%A Jeonghwa Lee
%A Chi-Hyuck Jun
%J Journal of Zhejiang University SCIENCE A
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A1001334

T1 - Biclustering of ARMA time series
A1 - Jeonghwa Lee
A1 - Chi-Hyuck Jun
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 12
SP - 959
EP - 965
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A1001334

biclustering is a method of grouping objects and attributes simultaneously in order to find multiple hidden patterns. When dealing with a long time series, there is a low possibility of finding meaningful clusters of whole time sequence. However, we may find more significant clusters containing partial time sequence by applying a biclustering method. This paper proposed a new biclustering algorithm for time series data following an autoregressive moving average (ARMA) model. We assumed the plaid model but modified the algorithm to incorporate the sequential nature of time series data. The maximum likelihood estimation (MLE) method was used to estimate coefficients of ARMA in each bicluster. We applied the proposed method to several synthetic data which were generated from different ARMA orders. Results from the experiments showed that the proposed method compares favorably with other biclustering methods for time series data.

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


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