Abstract
Recently, the Support Vector Regression (SVR) has been applied in the financial time series prediction. The financial data are usually highly noisy and contain outliers. Detecting outliers and deflating their influence are important but hard problems. In this paper, we propose a novel “two-phase” SVR training algorithm to detect outliers and reduce their negative impact. Our experimental results on three indices: Hang Seng Index, NASDAQ, and FSTE 100 index show that the proposed “two-phase” algorithm has improvement on the prediction.
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References
Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Cao, L.: Support Vector Machines Experts for Time Series Forecasting. Neurocompt. 51, 321–339 (2003)
Chang, C.-C., Lin, C.-J.: LIBSVM: A Library for Support Vector Machines (2004)
Chuang, C.-C., Su, S.-F., Jeng, J.-T., Hsiao, C.-C.: Robust support vector regression networks for function approximation with outliers. IEEE Transactions on Neural Networks 13, 1322–1330 (2002)
Mukherjee, S., Osuna, E., Girosi, F.: Nonlinear Prediction of Chaotic Time Series Using Support Vector Machines. In: Principe, J., Giles, L., Morgan, N., Wilson, E. (eds.) IEEE Workshop on Neural Networks for Signal Processing VII, pp. 511–519. IEEE Press, Los Alamitos (1997)
Suykens, J.A.K., De Brabanter, J., Lukas, L., Vandewalle, J.: Weighted Least Squares Support Vector Machines: Robustness and Sparse Approximation. Neurocompt. (2001)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.N., Golowich, S., Smola, A.: Support Vector Method for Function Approximation, Regression Estimation and Signal Processing. In: Mozer, M., Jordan, M., Petshe, T. (eds.) NIPS, vol. 9, pp. 281–287. MIT Press, Cambridge (1997)
Yang, H., Chan, L., King, I.: Support Vector Machine Regression for Volatile Stock Market Prediction. In: Yin, H., Allinson, N.M., Freeman, R., Keane, J.A., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, pp. 391–396. Springer, Heidelberg (2002)
Yang, H., King, I., Chan, L., Huang, K.: Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Support Vector Regression. In: Neural Information Processing: Research and Development, pp. 334–350. Springer, Heidelberg (2004)
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Yang, H., Huang, K., Chan, L., King, I., Lyu, M.R. (2004). Outliers Treatment in Support Vector Regression for Financial Time Series Prediction. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds) Neural Information Processing. ICONIP 2004. Lecture Notes in Computer Science, vol 3316. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30499-9_196
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DOI: https://doi.org/10.1007/978-3-540-30499-9_196
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23931-4
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