Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM)
American Journal of Applied Mathematics
Volume 3, Issue 3, June 2015, Pages: 112-117
Received: Mar. 31, 2015; Accepted: Apr. 14, 2015; Published: May 4, 2015
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Authors
Hong Zhang, School of Information, Beijing Wuzi University, Beijing, China
Li Zhou, School of Information, Beijing Wuzi University, Beijing, China
Jie Zhu, School of Information, Beijing Wuzi University, Beijing, China
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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.
Keywords
Phase Space Reconstruction Theory, Support Vector Regression, Financial Time Series Prediction
To cite this article
Hong Zhang, Li Zhou, Jie Zhu, Study on Financial Time Series Prediction Based on Phase Space Reconstruction and Support Vector Machine (SVM), American Journal of Applied Mathematics. Vol. 3, No. 3, 2015, pp. 112-117. doi: 10.11648/j.ajam.20150303.16
References
[1]
MA Jun-hai, Wang Zhi-qiang, and Chen Yu-shu. Prediction Techniques of Chaotic Time Series and Its Applications at Low Noise Level. Applied Mathematics and Mechanics,27(1):8-12, 2011.
[2]
Kugiuntzis D, Lingjrde 0 C, and Christophersen N. Regularized Local Linear Prediction of Chaotic Time Series. Physica D, 112(3-4): 345-359, 1998.
[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.
[6]
Patrick Koch, Bernd Bisch], and Oliver Flasch. Tuning and Evolution of Support Vector Kernels. Evoluliomary Intelligence, Vol. 5, No, 3, 3 59-150, 2012
[7]
Masayuki Karasuyama, Maoyuki Harada, Masashi Sugiyama, and Ichiro Takouchi. Multi-parametric Solution-path Algorithm for Instance-weighted Support Vector Machines. Machine Learning, Vol. 88, No. 3, 299-310, 2012.
[8]
S. Amari. Differential-geometrical Methods in Statistics. Springer-Verlag New York, 1985.
[9]
S. Amari. Neural Learning in Structured Parameter Spaces Natural Rjcmannian Gradient. Advances in Neural Information Processing Systems, (9):128 - 132, 997.
[10]
MC Mackey and L. Glass. Oscillation and Chaos in Physiological Control Systems. Science, 197(4300): 287, 1977.
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