• DocumentCode
    3275853
  • Title

    Crude Oil Price Forecasting with an Improved Model Based on Wavelet Transform and RBF Neural Network

  • Author

    Qunli, Wu ; Ge, Hao ; Xiaodong, Cheng

  • Author_Institution
    Dept. of Bus. Manage., North China Electr. Power Univ.(NCEPU), Baoding, China
  • Volume
    1
  • fYear
    2009
  • fDate
    15-17 May 2009
  • Firstpage
    231
  • Lastpage
    234
  • Abstract
    The fluctuation of oil price decides the security of energy and economics. So the crude oil price forecasting performs importantly. In the paper, we apply the improved model based on wavelet transform and radial basis function (RBF) neural network to forecast the future oil price. Wavelet transform decomposes the original price which is used as the output layer of RBF neural network and the parts of the decomposed are used as the input layer of neural network. The real data of Europe (UK) Brent blend spot price FOB (dollar per barrel) showed by Energy Information Administration (Official Energy Statistics from the U.S. Government) is used as the word crude oil price, dating from January 1997 to October 2008. Finally, the model is proved acutely and feasibly.
  • Keywords
    crude oil; forecasting theory; pricing; radial basis function networks; wavelet transforms; Energy Information Administration; Europe Brent blend spot price FOB; crude oil price forecasting; radial basis function neural network; wavelet transform; Economic forecasting; Europe; Fluctuations; Government; Neural networks; Petroleum; Power generation economics; Predictive models; Statistics; Wavelet transforms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information Technology and Applications, 2009. IFITA '09. International Forum on
  • Conference_Location
    Chengdu
  • Print_ISBN
    978-0-7695-3600-2
  • Type

    conf

  • DOI
    10.1109/IFITA.2009.36
  • Filename
    5231578