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
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;
Conference_Titel :
Information Technology and Applications, 2009. IFITA '09. International Forum on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3600-2
DOI :
10.1109/IFITA.2009.36
