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A hybrid intelligent system for forecasting crude oil price

Author

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  • Mohsen Mehrara
  • Hamid Abrishami
  • Mehdi Ahrari
  • Vida Varahrami

Abstract

In this paper, a novel hybrid intelligent framework is developed by applying a systematic integration of group method of data handling (GMDH) neural networks with genetic algorithm and rule-based expert system with web-based text mining for crude oil price forecasting. Our research reveals that employing a hybrid intelligent framework for crude oil price forecasting provides more accurate results than those obtained from GMDH neural networks when reviewing empirical data from this recent period of financial crisis and results will be so better when we employ hybrid intelligent system with generalised autoregressive conditional heteroskedasticity (GARCH) for crude oil price volatility forecasting. We can use from this method for other industries (gas, coal, ethanol, etc.).

Suggested Citation

  • Mohsen Mehrara & Hamid Abrishami & Mehdi Ahrari & Vida Varahrami, 2013. "A hybrid intelligent system for forecasting crude oil price," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 5(1), pages 1-16.
    • Handle: RePEc:ids:ijecbr:v:5:y:2013:i:1:p:1-16
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    Cited by:

    1. Flavio Barboza & Geraldo Nunes Silva & José Augusto Fiorucci, 2023. "A review of artificial intelligence quality in forecasting asset prices," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(7), pages 1708-1728, November.

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