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Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine

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  • Li Shu-rong
  • Ge Yu-lei

Abstract

A new accurate method on predicting crude oil price is presented, which is based on ε‐support vector regression (ε‐SVR) machine with dynamic correction factor correcting forecasting errors. We also propose the hybrid RNA genetic algorithm (HRGA) with the position displacement idea of bare bones particle swarm optimization (PSO) changing the mutation operator. The validity of the algorithm is tested by using three benchmark functions. From the comparison of the results obtained by using HRGA and standard RNA genetic algorithm (RGA), respectively, the accuracy of HRGA is much better than that of RGA. In the end, to make the forecasting result more accurate, the HRGA is applied to the optimize parameters of ε‐SVR. The predicting result is very good. The method proposed in this paper can be easily used to predict crude oil price in our life.

Suggested Citation

  • Li Shu-rong & Ge Yu-lei, 2013. "Crude Oil Price Prediction Based on a Dynamic Correcting Support Vector Regression Machine," Abstract and Applied Analysis, John Wiley & Sons, vol. 2013(1).
    • Handle: RePEc:wly:jnlaaa:v:2013:y:2013:i:1:n:528678
    DOI: 10.1155/2013/528678
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    References listed on IDEAS

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    1. Hunt, Benjamin & Isard, Peter & Laxton, Douglas, 2002. "The Macroeconomic Effects of Higher Oil Prices," National Institute Economic Review, National Institute of Economic and Social Research, vol. 179, pages 87-103, January.
    2. Sam Mirmirani & Hsi Cheng Li, 2004. "A Comparison Of Var And Neural Networks With Genetic Algorithm In Forecasting Price Of Oil," Advances in Econometrics, in: Applications of Artificial Intelligence in Finance and Economics, pages 203-223, Emerald Group Publishing Limited.
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    Cited by:

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