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Forecasting The Crude Oil Spot Price By Wavelet Neural Networks Using Oecd Petroleum Inventory Levels

Author

Listed:
  • YE PANG

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China)

  • WEI XU

    (School of Management, Graduate University of Chinese Academy of Sciences, Chinese Academy of Sciences, Beijing, 100190, China)

  • LEAN YU

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China)

  • JIAN MA

    (Department of Information Systems, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong)

  • KIN KEUNG LAI

    (Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong)

  • SHOUYANG WANG

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China)

  • SHANYING XU

    (Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China)

Abstract

In this study, a novel forecasting model based on the Wavelet Neural Network (WNN) is proposed to predict the monthly crude oil spot price. In the proposed model, the OECD industrial petroleum inventory level is used as an independent variable, and the Wavelet Neural Network (WNN) is used to explore the nonlinear relationship between inventories and the price. For verification purposes, the West Texas Intermediate (WTI) crude oil spot price is used for the tested target. Experimental results reveal that the WNN can model the nonlinear relationship between inventories and the price very well. Furthermore, the in-sample and out-of-sample prediction performance also demonstrates that the WNN-based forecasting model can produce more accurate prediction results than other nonlinear and linear models, even when the lengths of the forecast horizon are relatively short or long.

Suggested Citation

  • Ye Pang & Wei Xu & Lean Yu & Jian Ma & Kin Keung Lai & Shouyang Wang & Shanying Xu, 2011. "Forecasting The Crude Oil Spot Price By Wavelet Neural Networks Using Oecd Petroleum Inventory Levels," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 281-297.
    • Handle: RePEc:wsi:nmncxx:v:07:y:2011:i:02:n:s1793005711001937
    DOI: 10.1142/S1793005711001937
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    2. Lean Yu & Yueming Ma, 2021. "A Data-Trait-Driven Rolling Decomposition-Ensemble Model for Gasoline Consumption Forecasting," Energies, MDPI, vol. 14(15), pages 1-26, July.
    3. Dale Roberts & Laura Ryan, 2015. "Evidence of speculation in world oil prices," Australian Journal of Management, Australian School of Business, vol. 40(4), pages 630-651, November.

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