Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting
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, CHINA
2 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.

License

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

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

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

Publication date: 21 Nov 2017

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Article Downloads: 215

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