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Long Beach, CA, USA
Mar. 1, 2010 to Mar. 6, 2010
ISBN: 978-1-4244-5445-7
pp: 105-108
Xiaoyan Liu , Department of Computer Science&Software Engineering, University of Melbourne, Australia
Xindong Wu , School of Compupter Science&Information Enginerring, Hefei University of Technology, China
Huaiqing Wang , Department of Information Systems, City University of Hong Kong, HK
Rui Zhang , Department of Computer Science&Software Engineering, University of Melbourne, Australia
James Bailey , Department of Computer Science&Software Engineering, University of Melbourne, Australia
Kotagiri Ramamohanarao , Department of Computer Science&Software Engineering, University of Melbourne, Australia
ABSTRACT
Detecting changes in stock prices is a well known problem in finance with important implications for monitoring and business intelligence. Forewarning of changes in stock price, can be made by the early detection of changes in the distributions of stock order numbers. In this paper, we address the change detection problem for streams of stock order numbers and propose a novel incremental detection algorithm. Our algorithm gains high accuracy and low delay by employing a natural Poisson distribution assumption about the nature of stock order streams. We establish that our algorithm is highly scalable and has linear complexity. We also experimentally demonstrate its effectiveness for detecting change points, via experiments using both synthetic and real-world datasets.
CITATION
Xiaoyan Liu, Xindong Wu, Huaiqing Wang, Rui Zhang, James Bailey, Kotagiri Ramamohanarao, "Mining distribution change in stock order streams", ICDE, 2010, 2013 IEEE 29th International Conference on Data Engineering (ICDE), 2013 IEEE 29th International Conference on Data Engineering (ICDE) 2010, pp. 105-108, doi:10.1109/ICDE.2010.5447901
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