Abstract
LS-SVR is widely used in time series prediction. For LS-SVR, the selection of appropriate kernel function is a key issue, which has a great impact with the prediction accuracy. Compared with some other feasible kernel functions, Gaussian RBF is always selected as kernel function due to its good features. As a distance functions-based kernel function, Gaussian RBF also has some drawbacks. In this paper, we modified the standard Gaussian RBF to satisfy the two requirements of distance functions-based kernel functions which are fast damping at the place adjacent to the test point and keeping a moderate damping at infinity. The simulation results indicate preliminarily that the modified Gaussian RBF has better performance and can improve the prediction accuracy with LS-SVR.
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References
Müller, K.-R., Smola, A.J., Rätsch, G., Schölkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting Time Series with Support Vector Machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)
Tay, F.E.H., Cao, L.: Application of Support Vector Machines in Financial Time Series Forcasting. Omega 29, 309–317 (2001)
Zhang, J.F., Hu, S.S.: Chaotic Time Series Prediction Based on Multi-kernel Learning Support Vector Regression. ACTA Phys. Sinica (Chin. Phys.) 57, 2708–2713 (2008)
Guo, Y.M., Zhai, Z.J., Jiang, H.M.: Weighted Prediction of Multi-parameter Chaotic Time Series Using Least Squares Support Vector Regression (LS-SVR). J. Northwestern Polytechnical University 27, 83–86 (2009)
Zhang, X.R., Hu, L.Y., Wang, Z.S.: Multiple Kernel Support Vector Regression for Economic Forecasting. In: 2010 International Conference on Management Science and Engineering (17th), Melbourne, pp. 129–134 (2010)
Qi, M., Zhang, G.P.: Trend Time Series Modeling and Forecasting with Neural Networks. IEEE Trans. Neural Networks 19, 808–816 (2008)
Xi, J.H.: Study on Long-term Prediction Technology of Chaotic Time Series. Ph. D. Thesis of Dalian University of Technology (2005)
Vapnik, V.: Theory of Statistical Learning. In: Haykin, S. (ed.), pp. 375–563. A Wiley-Interscience Publication, John Wiley & Sons, Inc., New York (1998)
Suykens, J.A.K., Branbanter, J.K., Lukas, L., et al.: Weighted Least Squares Support Vector Machines: Robustness and Spare Approximation. Neurocomput. 48, 85–105 (2002)
Zhang, Y.: Enhanced Statistical Analysis of Nonlinear Process Using KPCA, KICA and SVM. Chem. Eng. Sci. 64, 801–811 (2009)
Xie, J.H.: LS-SVM Method Applied to Detect Damage for Piezoelectric Smart Structures. Sens. Actuat. 20, 164–167 (2007)
Xie, J.H.: Kernel optimization of LS-SVM Based on Damage Detection for Smart Structures. In: 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, pp. 4244–4519 (2009)
Yang, L., Zhang, L., Zhang, S.X., Liu, J.W.: Comparison Research of Single Kernel and Multi-kernel Relevance Vector Machine. Comput. Engineer. 36, 195–197 (2010)
Remaki, L., Cheriet, M.: KCS-New Kernel Family with Compact Support in Scale Space: Formulation and Impact. IEEE Trans. Image Process. 9, 970–981 (2009)
Huang, X.: The Study on Kernels in Support Vector Machine. Master degree thesis of Suzhou University (2008)
Ye, M.Y., Wang, X.D., Zhang, H.R.: Chaotic Time Series Forecasting Using Online Least Squares Support Vector Machine Regression. ACTA Phys. Sinica 54, 2568–2573 (2005)
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Guo, Y., Li, X., Bai, G., Ma, J. (2012). Time Series Prediction Method Based on LS-SVR with Modified Gaussian RBF. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7664. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34481-7_2
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DOI: https://doi.org/10.1007/978-3-642-34481-7_2
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