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Stock Index Prediction Based on the Analytical Center of Version Space

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Advances in Neural Networks - ISNN 2006 (ISNN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3973))

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Abstract

This paper presents a novel method for predicting the stock index based on the multiclass classification. The strategy firstly discretizes the stock index to different interval and assigns a class label to each interval, which yields a multiclass classification problem. After briefly reviewing multiclass classification algorithm, a multiclass classifier based on analytical center of version space is proposed and applied to stock index prediction. Experiments on shanghai stock index demonstrates that the strategy of stock index prediction proposed is validated and of practical value.

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Author information

Authors and Affiliations

  1. Institute of Computer and Communication, Hunan University, 410082, Changsha, China

    Fanzi Zeng & Yonghua Zhang

Authors
  1. Fanzi Zeng
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  2. Yonghua Zhang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd, 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Zeng, F., Zhang, Y. (2006). Stock Index Prediction Based on the Analytical Center of Version Space. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_67

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  • DOI: https://doi.org/10.1007/11760191_67

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34482-7

  • Online ISBN: 978-3-540-34483-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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