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Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting
Yu, Lean1; Zhang, Xun2; Wang, Shouyang2
2017-12-01
Source PublicationEURASIA JOURNAL OF MATHEMATICS SCIENCE AND TECHNOLOGY EDUCATION
ISSN1305-8215
Volume13Issue:12Pages:7893-7904
AbstractCrude 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.
Keywordsupport vector machines artificial neural networks ARIMA model ARFIMA model Markov-switching ARFIMA model
DOI10.12973/ejmste/77926
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[71433001] ; National Natural Science Foundation of China (NSFC)[71622011] ; National Natural Science Foundation of China (NSFC)[71301006] ; National Program for Support of Top Notch Young Professionals ; Beijing Advanced Innovation Center for Soft Matter Science and Engineering
WOS Research AreaEducation & Educational Research
WOS SubjectEducation & Educational Research
WOS IDWOS:000417628000027
PublisherISER PUBLICATIONS
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Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/29217
Collection系统科学研究所
Affiliation1.Beijing Univ Chem Technol, Sch Econ & Management, Beijing 100029, Peoples R China
2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Yu, Lean,Zhang, Xun,Wang, Shouyang. Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting[J]. EURASIA JOURNAL OF MATHEMATICS SCIENCE AND TECHNOLOGY EDUCATION,2017,13(12):7893-7904.
APA Yu, Lean,Zhang, Xun,&Wang, Shouyang.(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.
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 13.12(2017):7893-7904.
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