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A multiple adaptive wavelet recurrent neural network model to analyze crude oil prices

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Author Info

  • Mingming, Tang
  • Jinliang, Zhang

Abstract

International crude oil prices are an important part of the economy, and trends in changing oil prices have an effect on financial markets. Traditional hybrid analysis methods for international crude oil prices, such as wavelet transform and back propagation neural network (BPNN), seek synergy effects by sequentially filtering data through different models. However, these estimation methods cause loss of information through the introduction of biases in each filtering step, which are aggregated throughout the process when model assumptions are violated, and the traditional BPNN model does not have forecasting ability. In this study, we constructed a multiple wavelet recurrent neural network (MWRNN) simulation model, in which trend and random components of crude oil and gold prices were considered. The wavelet analysis was utilized to capture multiscale data characteristics, while a real neural network (RNN) was utilized to forecast crude oil prices at different scales. Finally, a standard BPNN was added to combine these independent forecasts from different scales into an optimal prediction of crude oil prices. The simulation results showed that the model has high prediction accuracy. The designed neural network is able to predict oil prices with an average error of 4.06% for testing and 3.88% for training data. This forecasting model would be able to predict the world crude oil prices with any commercial energy source prices instead of the gold prices.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economics and Business.

Volume (Year): 64 (2012)
Issue (Month): 4 ()
Pages: 275-286

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Handle: RePEc:eee:jebusi:v:64:y:2012:i:4:p:275-286

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Web page: http://www.elsevier.com/locate/jeconbus

Related research

Keywords: Multiple wavelet recurrent neural network; Crude oil price forecasting; Gold price;

References

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  1. de Souza e Silva, Edmundo G. & Legey, Luiz F.L. & de Souza e Silva, Edmundo A., 2010. "Forecasting oil price trends using wavelets and hidden Markov models," Energy Economics, Elsevier, vol. 32(6), pages 1507-1519, November.
  2. Mills, Terence C., 2004. "Statistical analysis of daily gold price data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 559-566.
  3. 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.
  4. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
  5. Paul Stevens, 2005. "Oil Markets," Oxford Review of Economic Policy, Oxford University Press, vol. 21(1), pages 19-42, Spring.
  6. Stephen P. A. Brown & Mine K. YĆ¼cel, 2001. "Energy prices and aggregate economic activity: an interpretive survey," Working Papers 0102, Federal Reserve Bank of Dallas.
  7. Gulen, S. Gurcan, 1998. "Efficiency in the crude oil futures market," Journal of Energy Finance & Development, Elsevier, vol. 3(1), pages 13-21.
  8. Donald W. Jones, Paul N. Leiby and Inja K. Paik, 2004. "Oil Price Shocks and the Macroeconomy: What Has Been Learned Since 1996," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-32.
  9. Abramson, Bruce & Finizza, Anthony, 1991. "Using belief networks to forecast oil prices," International Journal of Forecasting, Elsevier, vol. 7(3), pages 299-315, November.
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