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Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)

Received: 31 March 2015     Accepted: 14 April 2015     Published: 4 May 2015
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Abstract

Analyzing and forecasting the financial market based on the theory of phase space reconstruction of support vector regression. The key point of the phase space reconstruction is to choose the optimal delay time, and to find the optimal embedding dimension of space. This paper proposes the use of false nearest neighbor method to construct the error function for all the variables to determine the appropriate embedding dimension combinations. Kernel function in the SVR is an important factor for algorithm performance. Experiments show that the theory of phase space reconstruction based on support vector regression has a certain degree of predictive ability of market value at risk.

Published in American Journal of Applied Mathematics (Volume 3, Issue 3)
DOI 10.11648/j.ajam.20150303.16
Page(s) 112-117
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2015. Published by Science Publishing Group

Keywords

Phase Space Reconstruction Theory, Support Vector Regression, Financial Time Series Prediction

References
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[3] Qingfang neng and Yuhua Peng. A New Local Linear Predict ion Model for Chaotic Time Series. Physics Letters A, 370(5-6): 468-489, 2011.
[4] Chi-Jie Lu, Tiar Shyug Lee, and Chih-Chou Chiu. Financia Time Series Forecasting Using Independent Conponent Analysis and Support Vector Machine. Decision Support systems, 47(2009):118-124, 2009.
[5] Lam Hong Lee, Rajparsaci Rajkumar, and Dino lsa. Automatic Folder Allocation System Using Bayesian-Support Vector Macines Hybird Classification Approach. Applied Intelligence, Vol. 36, No. 2, 298-305, 2012.
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  • APA Style

    Hong Zhang, Li Zhou, Jie Zhu. (2015). Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM). American Journal of Applied Mathematics, 3(3), 112-117. https://doi.org/10.11648/j.ajam.20150303.16

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    ACS Style

    Hong Zhang; Li Zhou; Jie Zhu. Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM). Am. J. Appl. Math. 2015, 3(3), 112-117. doi: 10.11648/j.ajam.20150303.16

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    AMA Style

    Hong Zhang, Li Zhou, Jie Zhu. Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM). Am J Appl Math. 2015;3(3):112-117. doi: 10.11648/j.ajam.20150303.16

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  • @article{10.11648/j.ajam.20150303.16,
      author = {Hong Zhang and Li Zhou and Jie Zhu},
      title = {Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)},
      journal = {American Journal of Applied Mathematics},
      volume = {3},
      number = {3},
      pages = {112-117},
      doi = {10.11648/j.ajam.20150303.16},
      url = {https://doi.org/10.11648/j.ajam.20150303.16},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajam.20150303.16},
      abstract = {Analyzing and forecasting the financial market based on the theory of phase space reconstruction of support vector regression. The key point of the phase space reconstruction is to choose the optimal delay time, and to find the optimal embedding dimension of space. This paper proposes the use of false nearest neighbor method to construct the error function for all the variables to determine the appropriate embedding dimension combinations. Kernel function in the SVR is an important factor for algorithm performance. Experiments show that the theory of phase space reconstruction based on support vector regression has a certain degree of predictive ability of market value at risk.},
     year = {2015}
    }
    

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    T1  - Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)
    AU  - Hong Zhang
    AU  - Li Zhou
    AU  - Jie Zhu
    Y1  - 2015/05/04
    PY  - 2015
    N1  - https://doi.org/10.11648/j.ajam.20150303.16
    DO  - 10.11648/j.ajam.20150303.16
    T2  - American Journal of Applied Mathematics
    JF  - American Journal of Applied Mathematics
    JO  - American Journal of Applied Mathematics
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    EP  - 117
    PB  - Science Publishing Group
    SN  - 2330-006X
    UR  - https://doi.org/10.11648/j.ajam.20150303.16
    AB  - Analyzing and forecasting the financial market based on the theory of phase space reconstruction of support vector regression. The key point of the phase space reconstruction is to choose the optimal delay time, and to find the optimal embedding dimension of space. This paper proposes the use of false nearest neighbor method to construct the error function for all the variables to determine the appropriate embedding dimension combinations. Kernel function in the SVR is an important factor for algorithm performance. Experiments show that the theory of phase space reconstruction based on support vector regression has a certain degree of predictive ability of market value at risk.
    VL  - 3
    IS  - 3
    ER  - 

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Author Information
  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

  • School of Information, Beijing Wuzi University, Beijing, China

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