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Data Mining Methods for Crude Oil Market Analysis and Forecast
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Author(s): Jue Wang (Chinese Academy of Sciences, China), Wei Xu (Renmin University, China), Xun Zhang (Chinese Academy of Sciences, China), Yejing Bao (Beijing University of Technology, China), Ye Pang (The People’s Insurance Company (Group) of China, China)and Shouyang Wang (Chinese Academy of Sciences, China)
Copyright: 2010
Pages: 20
Source title:
Data Mining in Public and Private Sectors: Organizational and Government Applications
Source Author(s)/Editor(s): Antti Syvajarvi (University of Lapland, Finland)and Jari Stenvall (Tampere University, Finland)
DOI: 10.4018/978-1-60566-906-9.ch010
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Abstract
In this study, two data mining based models are proposed for crude oil price analysis and forecasting, one of which is a hybrid wavelet decomposition and support vector Machine (SVM) model and the other is an OECD petroleum inventory levels based wavelet neural network model (WNN). These models utilize support vector regression (SVR) and artificial neural network (ANN) technique for crude oil prediction and are made comparison with other forecasting models, respectively. Empirical results show that the proposed nonlinear models can improve the performance of oil price forecasting. The findings of this research are useful for private organizations and governmental agencies to take either preventive or corrective actions to reduce the impact of large fluctuation in crude oil markets, and demonstrate that the implications of data mining in public and private sectors and government agencies are promising for analyzing and predicting on the basis of data.
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