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Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm

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  • Yu, Lean
  • Wang, Shouyang
  • Lai, Kin Keung

Abstract

In this study, an empirical mode decomposition (EMD) based neural network ensemble learning paradigm is proposed for world crude oil spot price 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 a three-layer feed-forward neural network (FNN) model was used to model each of the extracted IMFs, so that the tendencies of these IMFs could be accurately predicted. Finally, the prediction results of all IMFs are combined with an adaptive linear neural network (ALNN), to formulate an ensemble output for the original crude oil price series. For verification and testing, 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 the proposed EMD-based neural network ensemble learning methodology. Empirical results obtained demonstrate attractiveness of the proposed EMD-based neural network ensemble learning paradigm.

Suggested Citation

  • Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    • Handle: RePEc:eee:eneeco:v:30:y:2008:i:5:p:2623-2635
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    References listed on IDEAS

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    1. Hillard G. Huntington, 1994. "Oil Price Forecasting in the 1980s: What Went Wrong?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-22.
    2. Gulen, S. Gurcan, 1998. "Efficiency in the crude oil futures market," Journal of Energy Finance & Development, Elsevier, vol. 3(1), pages 13-21.
    3. Abramson, Bruce & Finizza, Anthony, 1995. "Probabilistic forecasts from probabilistic models: A case study in the oil market," International Journal of Forecasting, Elsevier, vol. 11(1), pages 63-72, March.
    4. Lean Yu & Shouyang Wang & Kin Keung Lai, 2007. "Foreign-Exchange-Rate Forecasting With Artificial Neural Networks," International Series in Operations Research and Management Science, Springer, number 978-0-387-71720-3, December.
    5. Abosedra, Salah & Baghestani, Hamid, 2004. "On the predictive accuracy of crude oil futures prices," Energy Policy, Elsevier, vol. 32(12), pages 1389-1393, August.
    6. Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
    7. Philip K. Verleger, Jr., 1993. "Adjusting to Volatile Energy Prices," Peterson Institute Press: All Books, Peterson Institute for International Economics, number 41, April.
    8. Lanza, Alessandro & Manera, Matteo & Giovannini, Massimo, 2005. "Modeling and forecasting cointegrated relationships among heavy oil and product prices," Energy Economics, Elsevier, vol. 27(6), pages 831-848, November.
    9. Michael Ye & John Zyren & Joanne Shore, 2002. "Forecasting crude oil spot price using OECD petroleum inventory levels," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 8(4), pages 324-333, November.
    10. Ye, Michael & Zyren, John & Shore, Joanne, 2006. "Forecasting short-run crude oil price using high- and low-inventory variables," Energy Policy, Elsevier, vol. 34(17), pages 2736-2743, November.
    11. Ye, Michael & Zyren, John & Shore, Joanne, 2005. "A monthly crude oil spot price forecasting model using relative inventories," International Journal of Forecasting, Elsevier, vol. 21(3), pages 491-501.
    12. Shambora, William E. & Rossiter, Rosemary, 2007. "Are there exploitable inefficiencies in the futures market for oil?," Energy Economics, Elsevier, vol. 29(1), pages 18-27, January.
    13. Alvarez-Ramirez, Jose & Soriano, Angel & Cisneros, Myriam & Suarez, Rodolfo, 2003. "Symmetry/anti-symmetry phase transitions in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 583-596.
    14. Tang, Linghui & Hammoudeh, Shawkat, 2002. "An empirical exploration of the world oil price under the target zone model," Energy Economics, Elsevier, vol. 24(6), pages 577-596, November.
    15. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
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