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Received: 2008-06-12

Revision Accepted: 2008-11-25

Crosschecked: 2009-05-08

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Journal of Zhejiang University SCIENCE A 2009 Vol.10 No.7 P.937~951


Monitoring correlative financial data streams by local pattern similarity

Author(s):  Tao JIANG, Yu-cai FENG, Bin ZHANG, Zhong-sheng CAO, Ge FU, Jie SHI

Affiliation(s):  College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China; more

Corresponding email(s):   jiangtao_albert@yahoo.cn, fyc@dameng.com

Key Words:  Data mining, Model, Data streams, Correlation, Local pattern, Pattern similarity

Tao JIANG, Yu-cai FENG, Bin ZHANG, Zhong-sheng CAO, Ge FU, Jie SHI. Monitoring correlative financial data streams by local pattern similarity[J]. Journal of Zhejiang University Science A, 2009, 10(7): 937~951.

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author="Tao JIANG, Yu-cai FENG, Bin ZHANG, Zhong-sheng CAO, Ge FU, Jie SHI",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Monitoring correlative financial data streams by local pattern similarity
%A Yu-cai FENG
%A Zhong-sheng CAO
%A Ge FU
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%J Journal of Zhejiang University SCIENCE A
%V 10
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%@ 1673-565X
%D 2009
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0820445

T1 - Monitoring correlative financial data streams by local pattern similarity
A1 - Tao JIANG
A1 - Yu-cai FENG
A1 - Bin ZHANG
A1 - Zhong-sheng CAO
A1 - Ge FU
A1 - Jie SHI
J0 - Journal of Zhejiang University Science A
VL - 10
IS - 7
SP - 937
EP - 951
%@ 1673-565X
Y1 - 2009
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0820445

Developing tools for monitoring the correlations among thousands of financial data streams in an online fashion can be interesting and useful work. We aimed to find highly correlative financial data streams in local patterns. A novel distance metric function slope duration distance (SDD) is proposed, which is compatible with the characteristics of actual financial data streams. Moreover, a model monitoring correlations among local patterns (MCALP) is presented, which dramatically decreases the computational cost using an algorithm quickly online segmenting and pruning (QONSP) with O(1) time cost at each time tick t, and our proposed new grid structure. Experimental results showed that MCALP provides an improvement of several orders of magnitude in performance relative to traditional naive linear scan techniques and maintains high precision. Furthermore, the model is incremental, parallelizable, and has a quick response time.

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


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