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Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling

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  • Jammazi, Rania
  • Aloui, Chaker

Abstract

Oil price prediction has usually proved to be an intractable task due to the intrinsic complexity of oil market mechanism. In addition, the recent oil shock and its consequences relaunch the debate on understanding the behavior underlying the expected oil prices. Combining the dynamic properties of multilayer back propagation neural network and the recent Harr A trous wavelet decomposition, a Hybrid model HTW-MPNN is implemented to achieve prominent prediction of crude oil price. While recent studies focus on the determination of the best forecasting model by comparing various neural architectures or applying several decomposition techniques to the ANN, the new insight of this paper is to target the issue of the transfer function selection providing robust simulations on both in sample and out of sample basis. Based on the work of Yonaba, H., Anctil, F., and Fortin, V. (2010) “Comparing Sigmoid Transfer Functions for Neural Network Multistep Ahead Stream flow forecasting”. Journal of Hydrologic Engineering, April, 275–283, we use three variants of activation function namely sigmoid, bipolar sigmoid and hyperbolic tangent in order to test the model's flexibility. Furthermore, the forecasting robustness is checked through several levels of input–hidden nodes. Comparatively, results of HTW-MBPNN perform better than the conventional BPNN. Our conclusions add a major attribute to the previous studies corroborating the Occam razor's principle, especially when simulations are constructed through training and testing phases simultaneously. Finally, more eligible forecasting power is found according to the wavelet oil price signal which appears to be the closest to the real anticipations of future oil price fluctuations.

Suggested Citation

  • Jammazi, Rania & Aloui, Chaker, 2012. "Crude oil price forecasting: Experimental evidence from wavelet decomposition and neural network modeling," Energy Economics, Elsevier, vol. 34(3), pages 828-841.
  • Handle: RePEc:eee:eneeco:v:34:y:2012:i:3:p:828-841
    DOI: 10.1016/j.eneco.2011.07.018
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    References listed on IDEAS

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