An EMD-Based Neural Network Ensemble Learning Model for World Crude Oil Spot Price Forecasting

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning model is proposed for world crude oil spot price modeling and forecasting. For this purpose, the original crude oil spot price series were first decomposed into a finite and often small number of intrinsic mode functions (IMFs). Then the three-layer feed-forward neural network (FNN) model was used to model each extracted IMFs so that the tendencies of these IMFs can be accurately predicted. Finally, the prediction results of each IMFs are combined with an adaptive linear neural network (ALNN) to formulate a ensemble output for the original oil series. For verification, two main crude oil price series, West Texas Intermediate (WTI) crude oil spot price and Brent crude oil spot price are used to test the effectiveness of this proposed neural network ensemble methodology.

[1]  G. C. Watkins,et al.  How volatile are crude oil prices , 1994 .

[2]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[3]  Paul Stevens,et al.  The determination of oil prices 1945–1995 , 1995 .

[4]  Jose Alvarez-Ramirez,et al.  Symmetry/anti-symmetry phase transitions in crude oil markets , 2003 .

[5]  Kin Keung Lai,et al.  A novel nonlinear ensemble forecasting model incorporating GLAR and ANN for foreign exchange rates , 2005, Comput. Oper. Res..

[6]  Hillard G. Huntington,et al.  Oil Price Forecasting in the 1980s: What Went Wrong?* , 1994 .

[7]  Claudio Morana,et al.  A semiparametric approach to short-term oil price forecasting , 2001 .

[8]  Halbert White,et al.  Connectionist nonparametric regression: Multilayer feedforward networks can learn arbitrary mappings , 1990, Neural Networks.

[9]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[10]  Martin T. Hagan,et al.  Neural network design , 1995 .

[11]  Ronald Hagen,et al.  How is the international price of a particular crude determined , 1994 .

[12]  Lean Yu,et al.  A New Method for Crude Oil Price Forecasting Based on Support Vector Machines , 2006, International Conference on Computational Science.

[13]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[14]  Kin Keung Lai,et al.  CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY ∗ , 2005 .

[15]  Stephen Gilmore,et al.  Combining Measurement and Stochastic Modelling to Enhance Scheduling Decisions for a Parallel Mean Value Analysis Algorithm , 2006, International Conference on Computational Science.

[16]  Jr. Philip K. Verleger,et al.  Adjusting to volatile energy prices , 1994 .

[17]  Bruce Abramson,et al.  Probabilistic forecasts from probabilistic models: A case study in the oil market , 1995 .

[18]  Bruce Abramson,et al.  Using belief networks to forecast oil prices , 1991 .

[19]  Hamid Baghestani,et al.  On the predictive accuracy of crude oil futures prices , 2004 .