Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

Lean Yu; Xun Zhang; Shouyang Wang

doi:10.12973/ejmste/77926

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Lean Yu ¹ ^* , Xun Zhang ², Shouyang Wang ²

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¹ School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, CHINA² Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, CHINA^* Corresponding Author

This article belongs to the special issue "Problems of Application Analysis in Knowledge Management and Science-Mathematics-Education".

Abstract

Crude oil price forecasting is one of the most important topics in the field of energy research. Accordingly, numerous methods such as statistical, econometrical and intelligent approaches are applied for crude oil price forecasting. In this paper, a typical competitive learning algorithm, support vector machine (SVM), is empirically investigated to verify the feasibility and potentiality of SVM in crude oil price forecasting. For this purpose, five different prediction models, feed-forward neural networks (FNN), auto-regressive integrated moving average (ARIMA) model, fractional integrated ARIMA (ARFIMA) model, Markov-switching ARFIMA (MS-ARFIMA) model, and random walk (RW) model are used in the study. Experimental results obtained show that the SVM model outperforms the other five methods, implying that it is a fairly good candidate for crude oil price forecasting in terms of either one-step prediction or multi-step prediction.

Keywords

support vector machines
artificial neural networks
ARIMA model
ARFIMA model
Markov-switching ARFIMA model

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2017, Volume 13, Issue 12, 7893-7904

https://doi.org/10.12973/ejmste/77926

Publication date: 21 Nov 2017

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Article Downloads: 1508

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References

Abramson, B., & Finizza, A. (1995). Probabilistic forecasts from probabilistic models: a case study in the oil market. International Journal of Forecasting, 11(1), 63-72.
Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2, 121-167.
Cao, L. J., & Tay, F. E. H. (2001). Financial forecasting using support vector machines. Neural Computing Applications, 10, 184-192.
Cristianini, N., & Taylor, J. S. (2000). An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, New York.
Chou, J. R. (2016). An Empirical Study of User Experience on Touch Mice. Eurasia Journal of Mathematics, Science & Technology Education, 12(11), 2875-2885.
Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13, 253-263.
Doornik, J. A., & Ooms, M. (2001). A package for estimating, forecasting and simulating ARFIMA models: ARFIMA Package 1.1 for Ox. Working paper, Nuffield College, Oxford.
Granger, C. W. J., & Joyeux, R. (1980). An introduction to long-memory time series models and fractional differencing. Journal of Time Series Analysis, 1, 15-29.
Gulen, S. G. (1998). Efficiency in the crude oil futures market. Journal of Energy Finance & Development, 3, 13-21.
Hamilton, J. D. (1994). Time Series Analysis. Princeton, NJ: Princeton University Press.
Huntington, H. G. (1994). Oil price forecasting in the 1980s: what went wrong? The Energy Journal, 15(2), 1-22.
Kaboudan, M. A. (2001). Compumetric forecasting of crude oil prices. The Proceedings of IEEE Congress on Evolutionary Computation, 283-287.
Krolzig, H. M. (1998). Econometric modelling of markov-switching vector autoregressions using MSVAR for Ox, Working Paper, Nuffield College, Oxford.
Lanza, A., Manera, M., & Giovannini, M. (2005). Modeling and forecasting cointegrated relationships among heavy oil and product prices. Energy Economics, 27, 831-848.
Lee, D. K., & Lee, E. S. (2016). Analyzing team based engineering design process in computer supported collaborative learning. Eurasia Journal of Mathematics, Science & Technology Education, 12(4), 767-782.
Mirmirani, S., & Li, H. C. (2004). A comparison of VAR and neural networks with genetic algorithm in forecasting price of oil. Advances in Econometrics, 19, 203-223.
Morana, C. (2001). A semiparametric approach to short-term oil price forecasting. Energy Economics, 23(3), 325-338.
Muller, K. R., Smola, J. A., & Scholkopf, B. (1997). Prediction time series with support vector machines. Proceedings of International Conference on Artificial Neural Networks, Lausanne, 999-1004.
Panas, E., & Ninni, V. (2000). Are oil markets chaotic? A non-linear dynamic analysis. Energy Economics, 22, 549-568.
Pelckmans, K., Suykens, J. A. K., Van Gestel, T., De Brabanter, J., Lukas, L., Hamers, B., De Moor, B., & Vandewalle, J. (2003). LS-SVMlab Toolbox User’s Guide (version 1.5), ESAT-SCD-SISTA Technical Report 02-145, Katholieke Universiteit Leuven.
Schmidt, C. M., & Tschernig, R. (1995). The identification of fractional ARIMA models. Sonderforschungsbereich, 373, Humboldt Universitaet Berlin.
Shambora, W. E., & Rossiter, R. (2007). Are there exploitable inefficiencies in the futures market for oil? Energy Economics, 29, 18-27.
Sowell, F. (1992). Maximum likelihood estimation of stationary univariate fractionally integrated time series models. Journal of Econometrics, 53, 165-188.
Tay, F. E. H., & Cao, L. J. (2001a). Applications of support vector machines in financial time series forecasting. Omega, 29, 309-317.
Tay, F. E. H., & Cao, L. J. (2001b). Improved financial time series forecasting by combining support vector machines with self-organizing feature map. Intelligent Data Analysis, 5, 339-354.
Tay, F. E. H., & Cao, L. J. (2002). Modified support vector machines in financial time series forecasting. Neurocomputing, 48, 847-861.
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer.
Vapnik, V. N. (1999). An overview of statistical learning theory. IEEE Transactions on Neural Networks, 10, 988-999.
Vapnik, V. N., Golowich, S. E., & Smola, A. J. (1996). Support vector method for function approximation, regression estimation, and signal processing. Advances in Neural Information Processing Systems, 9, 281-287.
Wang, S. Y., Yu, L., & Lai, K. K. (2004). A novel hybrid AI system framework for crude oil price forecasting. Lecture Notes in Computer Science, 3327, 233-242.
Wang, S. Y., Yu, L., & Lai, K. K. (2005). Crude oil price forecasting with TEI@I methodology. Journal of Systems Sciences and Complexity, 18(2), 145-166.
Watkins, G. C., & Plourde, A. (1994). How volatile are crude oil prices? OPEC Review, 18(4), 220-245.
Weigend, A. S., & Gershenfeld, N. A. (1994). Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, Reading, MA.
West, K. D. (1996). Asymptotic inference about predictive ability. Econometrica, 64, 1067-1084.
White, H. (1990). Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings. Neural Networks, 3, 535-549.
Ye, M., Zyren, J., & Shore, J. (2002). Forecasting crude oil spot price using OECD petroleum inventory levels. International Advances in Economic Research, 8, 324-334.
Ye, M., Zyren, J., & Shore, J. (2005). A monthly crude oil spot price forecasting model using relative inventories. International Journal of Forecasting, 21, 491-501.
Ye, M., Zyren, J., & Shore, J. (2006). Forecasting short-run crude oil price using high and low-inventory variables. Energy Policy, 34, 2736-2743.
Yu, L., Lai, K. K., Wang, S. Y., & He, K. J. (2007a). Oil price forecasting with an EMD-based multiscale neural network learning paradigm. Lecture Notes in Computer Science, 4489, 925-932.
Yu, L., Wang, S. Y., & Lai, K. K. (2007b). Foreign-Exchange-Rate Forecasting with Artificial Neural Networks. Springer, New York.
Yu, L., Wang, S. Y., & Lai, K. K. (2008). Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm. Energy Economics, 30(5), 2623-2635.
Zhang, X., Lai, K. K., & Wang, S. Y. (2008). A new approach for crude oil price analysis based on empirical mode decomposition. Energy Economics, 30(3), 905-918.

How to cite this article

APA

Yu, L., Zhang, X., & Wang, S. (2017). Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting. Eurasia Journal of Mathematics, Science and Technology Education, 13(12), 7893-7904. https://doi.org/10.12973/ejmste/77926

Vancouver

Yu L, Zhang X, Wang S. Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting. EURASIA J Math Sci Tech Ed. 2017;13(12):7893-904. https://doi.org/10.12973/ejmste/77926

AMA

Yu L, Zhang X, Wang S. Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting. EURASIA J Math Sci Tech Ed. 2017;13(12), 7893-7904. https://doi.org/10.12973/ejmste/77926

Chicago

Yu, Lean, Xun Zhang, and Shouyang Wang. "Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting". Eurasia Journal of Mathematics, Science and Technology Education 2017 13 no. 12 (2017): 7893-7904. https://doi.org/10.12973/ejmste/77926

Harvard

Yu, L., Zhang, X., and Wang, S. (2017). Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting. Eurasia Journal of Mathematics, Science and Technology Education, 13(12), pp. 7893-7904. https://doi.org/10.12973/ejmste/77926

MLA

Yu, Lean et al. "Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting". Eurasia Journal of Mathematics, Science and Technology Education, vol. 13, no. 12, 2017, pp. 7893-7904. https://doi.org/10.12973/ejmste/77926