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A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting

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  • Yu, Lean
  • Wang, Zishu
  • Tang, Ling

Abstract

To enhance prediction accuracy and reduce computation complexity, a decomposition–ensemble methodology with data-characteristic-driven reconstruction is proposed for crude oil price forecasting, based on two promising principles of “divide and conquer” and “data-characteristic-driven modeling”. Actually, this proposed model improves the existing decomposition–ensemble techniques in the “divide and conquer” framework, by formulating and incorporating a data-characteristic-driven reconstruction method based on the “data-characteristic-driven modeling”. Four main steps are involved in the proposed methodology, i.e., data decomposition for simplifying the complex data, component reconstruction based on the “data-characteristic-driven modeling” for capturing inner factors and reducing computational cost, individual prediction for each reconstructed component via a certain artificial intelligence (AI) tool, and ensemble prediction for final output. In the proposed data-characteristic-driven reconstruction, all decomposed modes are thoroughly analyzed to explore the hidden data characteristics, and are accordingly reconstructed into some meaningful components. For illustration and verification, the West Texas Intermediate (WTI) and Brent crude oil spot prices are used as the sample data, and the empirical results indicate that the proposed model statistically outperforms all considered benchmark models (including popular AI single models, typical decomposition–ensemble models without reconstruction, and similar decomposition–ensemble models with other existing reconstruction methods), since it has higher prediction accuracy and less computational time.

Suggested Citation

  • Yu, Lean & Wang, Zishu & Tang, Ling, 2015. "A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting," Applied Energy, Elsevier, vol. 156(C), pages 251-267.
  • Handle: RePEc:eee:appene:v:156:y:2015:i:c:p:251-267
    DOI: 10.1016/j.apenergy.2015.07.025
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    References listed on IDEAS

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    Cited by:

    1. repec:eee:energy:v:154:y:2018:i:c:p:328-336 is not listed on IDEAS
    2. Ju, Keyi & Su, Bin & Zhou, Dequn & Zhou, P. & Zhang, Yuqiang, 2015. "Oil price crisis response: Capability assessment and key indicator identification," Energy, Elsevier, vol. 93(P2), pages 1353-1360.
    3. repec:gam:jsusta:v:9:y:2017:i:12:p:2299-:d:122514 is not listed on IDEAS
    4. repec:gam:jeners:v:10:y:2017:i:9:p:1422-:d:112222 is not listed on IDEAS
    5. repec:gam:jeners:v:10:y:2017:i:11:p:1862-:d:118741 is not listed on IDEAS
    6. repec:gam:jeners:v:11:y:2018:i:7:p:1882-:d:158756 is not listed on IDEAS
    7. Taiyong Li & Min Zhou & Chaoqi Guo & Min Luo & Jiang Wu & Fan Pan & Quanyi Tao & Ting He, 2016. "Forecasting Crude Oil Price Using EEMD and RVM with Adaptive PSO-Based Kernels," Energies, MDPI, Open Access Journal, vol. 9(12), pages 1-21, December.
    8. repec:eee:appene:v:205:y:2017:i:c:p:336-344 is not listed on IDEAS
    9. repec:eee:phsmap:v:501:y:2018:i:c:p:98-110 is not listed on IDEAS
    10. repec:eee:energy:v:148:y:2018:i:c:p:49-58 is not listed on IDEAS
    11. Wang, Deyun & Luo, Hongyuan & Grunder, Olivier & Lin, Yanbing & Guo, Haixiang, 2017. "Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm," Applied Energy, Elsevier, vol. 190(C), pages 390-407.
    12. Ju, Keyi & Su, Bin & Zhou, Dequn & Wu, Junmin & Liu, Lifan, 2016. "Macroeconomic performance of oil price shocks: Outlier evidence from nineteen major oil-related countries/regions," Energy Economics, Elsevier, vol. 60(C), pages 325-332.
    13. Ju, Keyi & Su, Bin & Zhou, Dequn & Zhang, Yuqiang, 2016. "An incentive-oriented early warning system for predicting the co-movements between oil price shocks and macroeconomy," Applied Energy, Elsevier, vol. 163(C), pages 452-463.
    14. Zhu, Bangzhu & Han, Dong & Wang, Ping & Wu, Zhanchi & Zhang, Tao & Wei, Yi-Ming, 2017. "Forecasting carbon price using empirical mode decomposition and evolutionary least squares support vector regression," Applied Energy, Elsevier, vol. 191(C), pages 521-530.
    15. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.

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