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An effective rolling decomposition-ensemble model for gasoline consumption forecasting

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  • Yu, Lean
  • Ma, Yueming
  • Ma, Mengyao

Abstract

In this paper, an effective rolling decomposition-ensemble model is proposed for quarterly gasoline consumption forecasting in China. In this model, three steps, data decomposition, component prediction and ensemble output, are involved. In the data decomposition, wavelet decomposition and ensemble empirical mode decomposition are used due to few assumptions and excellent performance. In the component prediction, support vector regression is adopted due to the global approximation capability for data scarcity issue. In the ensemble output, the simple addition strategy is used for final aggregation. In order to solve the illusion of high prediction accuracy caused by the decomposition of the test dataset, the rolling decomposition and forecasting mechanism are adopted in this methodology. For illustration and verification purpose, 30 provincially quarterly gasoline consumption data in China are used. The experimental results demonstrate the effectiveness and robustness of the proposed rolling decomposition-ensemble model for gasoline consumption forecasting in terms of the accuracy of level and directional prediction.

Suggested Citation

  • Yu, Lean & Ma, Yueming & Ma, Mengyao, 2021. "An effective rolling decomposition-ensemble model for gasoline consumption forecasting," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221001183
    DOI: 10.1016/j.energy.2021.119869
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    References listed on IDEAS

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    1. Yu, Lean & Zhao, Yang & Tang, Ling, 2014. "A compressed sensing based AI learning paradigm for crude oil price forecasting," Energy Economics, Elsevier, vol. 46(C), pages 236-245.
    2. Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2008. "Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm," Energy Economics, Elsevier, vol. 30(5), pages 2623-2635, September.
    3. Ye Pang & Wei Xu & Lean Yu & Jian Ma & Kin Keung Lai & Shouyang Wang & Shanying Xu, 2011. "Forecasting The Crude Oil Spot Price By Wavelet Neural Networks Using Oecd Petroleum Inventory Levels," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 281-297.
    4. Dantas, Tiago Mendes & Cyrino Oliveira, Fernando Luiz & Varela Repolho, Hugo Miguel, 2017. "Air transportation demand forecast through Bagging Holt Winters methods," Journal of Air Transport Management, Elsevier, vol. 59(C), pages 116-123.
    5. Park, Sung Y. & Zhao, Guochang, 2010. "An estimation of U.S. gasoline demand: A smooth time-varying cointegration approach," Energy Economics, Elsevier, vol. 32(1), pages 110-120, January.
    6. Yingrui Zhou & Taiyong Li & Jiayi Shi & Zijie Qian, 2019. "A CEEMDAN and XGBOOST-Based Approach to Forecast Crude Oil Prices," Complexity, Hindawi, vol. 2019, pages 1-15, February.
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    Cited by:

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    8. Fang, Tianhui & Zheng, Chunling & Wang, Donghua, 2023. "Forecasting the crude oil prices with an EMD-ISBM-FNN model," Energy, Elsevier, vol. 263(PA).
    9. Emmanouil Sofianos & Emmanouil Zaganidis & Theophilos Papadimitriou & Periklis Gogas, 2024. "Forecasting East and West Coast Gasoline Prices with Tree-Based Machine Learning Algorithms," Energies, MDPI, vol. 17(6), pages 1-14, March.
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