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A novel hybrid model based on deep learning and error correction for crude oil futures prices forecast

Author

Listed:
  • Wu, Junhao
  • Dong, Jinghan
  • Wang, Zhaocai
  • Hu, Yuan
  • Dou, Wanting

Abstract

Energy is a crucial basis for ensuring people's life quality and advancing social and economic development. Whereas, the crude oil futures prices are influenced by several factors such as energy policies, which shows strong non-linearity and high volatility. Accurately predicting price changes of crude oil futures is a challenging task. In this study, a new hybrid model, namely VILIG based on the "decomposition-prediction-ensemble-error correction" framework, is constructed to predict the closing prices of West Texas Intermediate (WTI) and Brent crude oil futures prices. First, variational modal decomposition (VMD) is applied to decompose the original price dataset into several sub-models. Then, each sub-model is used as an input to the Long-short term memory network (LSTM) and an improved golden jackal optimization (IGJO) algorithm is proposed to optimize the hyperparameters of the LSTM. Subsequently, an improved fully ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is introduced to perform a secondary decomposition of the error sequence and gate recurrent unit (GRU) is used to predict the decomposed error sequences separately. Finally, the prediction outcomes are obtained by linear aggregation. In addition, based on the point prediction results, this study presents kernel density estimation (KDE) for interval estimation to perform uncertainty prediction. The empirical results show that the VILIG model outperforms all the currently available major models in terms of quantitative evaluation metrics. The model can provide a data base for decision making for crude oil futures investors and market regulators.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:jrpoli:v:83:y:2023:i:c:s0301420723003136
    DOI: 10.1016/j.resourpol.2023.103602
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    1. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    2. Murat, Atilim & Tokat, Ekin, 2009. "Forecasting oil price movements with crack spread futures," Energy Economics, Elsevier, vol. 31(1), pages 85-90, January.
    3. Lyu, Yongjian & Wang, Peng & Wei, Yu & Ke, Rui, 2017. "Forecasting the VaR of crude oil market: Do alternative distributions help?," Energy Economics, Elsevier, vol. 66(C), pages 523-534.
    4. Faisal Saeed & Anand Paul & Hyuncheol Seo, 2022. "A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting," Energies, MDPI, vol. 15(6), pages 1-17, March.
    5. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
    6. Abdul Aziz Karia & Imbarine Bujang & Ismail Ahmad, 2013. "Fractionally integrated ARMA for crude palm oil prices prediction: case of potentially overdifference," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(12), pages 2735-2748, December.
    7. 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.
    8. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    9. E, Jianwei & Ye, Jimin & He, Lulu & Jin, Haihong, 2019. "Energy price prediction based on independent component analysis and gated recurrent unit neural network," Energy, Elsevier, vol. 189(C).
    10. Aloui, Riadh & Gupta, Rangan & Miller, Stephen M., 2016. "Uncertainty and crude oil returns," Energy Economics, Elsevier, vol. 55(C), pages 92-100.
    11. Afshin Javan & Carola Vallejo, 2016. "Fundamentals, non-fundamentals and the oil price changes in 2007–2009 and 2014–2015," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 40(2), pages 125-154, June.
    12. Gaye GENCER & Sercan DEMIRALAY, 2013. "The impact of oil prices on sectoral returns: an empirical analysis from Borsa Istanbul," Theoretical and Applied Economics, Asociatia Generala a Economistilor din Romania - AGER, vol. 0(12(589)), pages 7-24, December.
    13. Junhao Wu & Zhaocai Wang & Yuan Hu & Sen Tao & Jinghan Dong, 2023. "Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 937-953, January.
    14. Ding, Yishan, 2018. "A novel decompose-ensemble methodology with AIC-ANN approach for crude oil forecasting," Energy, Elsevier, vol. 154(C), pages 328-336.
    15. Xiaoyi Mu and Haichun Ye, 2015. "Small Trends and Big Cycles in Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    16. Zhu, Jiaming & Wu, Peng & Chen, Huayou & Liu, Jinpei & Zhou, Ligang, 2019. "Carbon price forecasting with variational mode decomposition and optimal combined model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 140-158.
    17. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
    18. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    19. repec:agr:journl:v:12(589):y:2013:i:12(589):p:7-24 is not listed on IDEAS
    20. Liu, Weiping & Wang, Chengzhu & Li, Yonggang & Liu, Yishun & Huang, Keke, 2021. "Ensemble forecasting for product futures prices using variational mode decomposition and artificial neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    21. Peng Xu & Muhammad Aamir & Ani Shabri & Muhammad Ishaq & Adnan Aslam & Li Li, 2020. "A New Approach for Reconstruction of IMFs of Decomposition and Ensemble Model for Forecasting Crude Oil Prices," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-23, October.
    22. Yin, Libo & Yang, Qingyuan, 2016. "Predicting the oil prices: Do technical indicators help?," Energy Economics, Elsevier, vol. 56(C), pages 338-350.
    23. Zhang, Kefei & Cao, Hua & Thé, Jesse & Yu, Hesheng, 2022. "A hybrid model for multi-step coal price forecasting using decomposition technique and deep learning algorithms," Applied Energy, Elsevier, vol. 306(PA).
    24. Zhou, Jianguo & Xu, Zhongtian, 2023. "A novel three-stage hybrid learning paradigm based on a multi-decomposition strategy, optimized relevance vector machine, and error correction for multi-step forecasting of precious metal prices," Resources Policy, Elsevier, vol. 80(C).
    25. Drachal, Krzysztof, 2021. "Forecasting crude oil real prices with averaging time-varying VAR models," Resources Policy, Elsevier, vol. 74(C).
    26. Kaijian He & Rui Zha & Jun Wu & Kin Keung Lai, 2016. "Multivariate EMD-Based Modeling and Forecasting of Crude Oil Price," Sustainability, MDPI, vol. 8(4), pages 1-11, April.
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