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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, Hindawi, vol. 2013, pages 1-7, March.
    • Handle: RePEc:hin:jnlaaa:528678
    DOI: 10.1155/2013/528678
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    Cited by:

    1. Karasu, Seçkin & Altan, Aytaç, 2022. "Crude oil time series prediction model based on LSTM network with chaotic Henry gas solubility optimization," Energy, Elsevier, vol. 242(C).
    2. Jiang Wu & Feng Miu & Taiyong Li, 2020. "Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market," Energies, MDPI, vol. 13(7), pages 1-20, April.

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