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A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction

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  • Niu, Hongli
  • Xu, Kunliang
  • Liu, Cheng

Abstract

Accurately forecasting the energy price has increasingly attracted attention of researchers. A novel hybrid forecasting model, termed as ICEEMDAN-R-AttGRU, is for the first time proposed to enhance the forecasting accuracy of energy price series (Brent oil, DCE coke and NYMEX natural gas). In the proposed model, the improved CEEMDAN is taken to decompose the raw prices into multiple subcomponents. Then, a novel regrouping method based on frequency is put forward to reconstruct the subcomponents to reduce the forecasting workload and diminish the chance of errors. The attention-based GRU network is adopted to perform the forecasting task for each component, in which the attention mechanism is applied to allocate and optimize weights to the input elements in GRU. The empirical results measured by various performance metrics verify that the predictive accuracy is evidently improved by ICEEMDAN-R-AttGRU model in most cases compared with single models, ICEEMDAN-based and ICEEMDAN-R-based hybrid models. Besides, a new multi-scale composite complexity synchronization (MCCS) statistic is introduced into model measurement, which further confirms the competitive prediction ability of ICEEMDAN-R-AttGRU in different exponent and time scales.

Suggested Citation

  • Niu, Hongli & Xu, Kunliang & Liu, Cheng, 2021. "A decomposition-ensemble model with regrouping method and attention-based gated recurrent unit network for energy price prediction," Energy, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:energy:v:231:y:2021:i:c:s0360544221011890
    DOI: 10.1016/j.energy.2021.120941
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    Cited by:

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    6. Xu, Kunliang & Wang, Weiqing, 2023. "Limited information limits accuracy: Whether ensemble empirical mode decomposition improves crude oil spot price prediction?," International Review of Financial Analysis, Elsevier, vol. 87(C).
    7. Huang, Zhiwen & Li, Tong & Huang, Kexin & Ke, Hanbing & Lin, Mei & Wang, Qiuwang, 2022. "Predictions of flow and temperature fields in a T-junction based on dynamic mode decomposition and deep learning," Energy, Elsevier, vol. 261(PA).
    8. Jiang, Ping & Liu, Zhenkun & Wang, Jianzhou & Zhang, Lifang, 2021. "Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective version of chaos game optimization algorithm," Resources Policy, Elsevier, vol. 73(C).
    9. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).

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