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A support vector machine-based ensemble prediction for crude oil price with VECM and STEPMRS

Author

Listed:
  • Dongkuan Xu
  • Tianjia Chen
  • Wei Xu

Abstract

Crude oil price prediction attracts more and more attentions, not only for its importance to the modern industry, but also for its complex price movement. This paper proposes a support vector machine-based ensemble model to forecast crude oil price based on VECM and Stochastic Time Effective Pattern Modelling and Recognition System (STEPMRS). In the proposed model, VECM is first used to model the trend of crude oil price, and then STEPMRS is offered to forecast errors. Finally, SVM is employed to integrate the results from the ones of VECM and STEPMRS to make the final forecasting values more accurate and desirable. The WTI spot price and a set of financial indicators are utilised as inputs for the validation purpose. The empirical results show that the proposed ensemble model can significantly improve the forecasting performance, compared with other 11 models in four aspects, and be an alternative tool to predict crude oil price.

Suggested Citation

  • Dongkuan Xu & Tianjia Chen & Wei Xu, 2015. "A support vector machine-based ensemble prediction for crude oil price with VECM and STEPMRS," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 38(1/2/3), pages 18-48.
    • Handle: RePEc:ids:ijgeni:v:38:y:2015:i:1/2/3:p:18-48
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    Citations

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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.
    2. Wu, Junhao & Dong, Jinghan & Wang, Zhaocai & Hu, Yuan & Dou, Wanting, 2023. "A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast," Resources Policy, Elsevier, vol. 83(C).

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