Abstract
Selection of kernel function parameters is one of the key problems in support vector regression(SVR) for forecasting because these free parameters have significant impact on the performances of forecasting accuracy. The commonly used grid search method is intractable and computational expensive. In this paper, a fast grid search method is proposed for tuning multiple parameters for SVR with RBF kernel for time series forecasting. Empirical results confirm the feasibility and validation of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Francis, E.H.T., Cao, L.: Application of support vector machines in financial time series forecasting. Omega: International Journal of Management Science 29, 309–317 (2001)
Kim, K.J.: Financial time series forecasting using support vector machines. Neurocomputing 55, 307–319 (2003)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. Technical Report, Department of Computer Science and Information Engineering, National Taiwan University (2004), Available at http://www.csie.ntu.edu.tw/cjlin/papers/guide/guide.pdf
Bao, Y.K., Lu, Y.S., Zhang, J.: Forecasting stock price by SVMs regression. In: Bussler, C.J., Fensel, D. (eds.) AIMSA 2004. LNCS (LNAI), vol. 3192, pp. 295–303. Springer, Heidelberg (2004)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Machine Learning 46, 131–159 (2002)
Boughorbel, S., Tarel, J.P., Boujemaa, N.: The LCCP for optimizing kernel parameters for SVM. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds.) ICANN 2005. LNCS (LNAI), vol. 3697, pp. 589–594. Springer, Heidelberg (2005)
Bo, L.F., Wang, L., et al.: Multiple parameter selection for LS-SVM using smooth leave-one-out error. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3496, pp. 851–856. Springer, Heidelberg (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bao, Y., Liu, Z. (2006). A Fast Grid Search Method in Support Vector Regression Forecasting Time Series. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_61
Download citation
DOI: https://doi.org/10.1007/11875581_61
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)