Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting
Lean Yu 1  
Xun Zhang 2
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School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, CHINA
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, CHINA
Lean Yu   

School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China, College of Economics and Finance City, Fujian Province, China, 362021 Quanzhou, China
Online publication date: 2017-11-21
Publication date: 2017-11-21
EURASIA J. Math., Sci Tech. Ed 2017;13(12):7893–7904
This article belongs to the special issue "Problems of Application Analysis in Knowledge Management and Science-Mathematics-Education".
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.
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