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An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction

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  • Wang, Jujie
  • Zhuang, Zhenzhen
  • Gao, Dongming

Abstract

Accurate prediction of carbon price is of great significance for promoting the steady development of carbon trading market. However, as the unstable and nonlinear characteristics of carbon prices, it is challenging to obtain accurate future transaction prices. Therefore, this paper proposes a hybrid system for carbon price prediction. Specifically, the two-stage data preprocessing can decompose and reconstruct the carbon price sequence, which can reduce noise and chaotic disturbance and improve the quality of input data. Moreover, extreme gradient boosting (XGBoost) and partial autocorrelation function (PACF) are proposed to screen the influencing factors and carbon price subsequences to obtain the optimal features. The prediction and combination module uses the strategy of subsequence prediction and nonlinear integration, leveraging the strengths of each model to achieve the ideal results. In this system, an effective slime mold algorithm (SMA) is used to ensure the accuracy and stability of the prediction system. The research results show that: (a) The developed system has the best predictive performance compared to the benchmark models. (b) Energy, economy and commodity markets have a more significant long-term impact on carbon prices, and news sentiment and Baidu index contribute to their short-term fluctuations.

Suggested Citation

  • Wang, Jujie & Zhuang, Zhenzhen & Gao, Dongming, 2023. "An enhanced hybrid model based on multiple influencing factors and divide-conquer strategy for carbon price prediction," Omega, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:jomega:v:120:y:2023:i:c:s0305048323000865
    DOI: 10.1016/j.omega.2023.102922
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    References listed on IDEAS

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    Cited by:

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    2. Liu, Longlong & Zhou, Suyu & Jie, Qian & Du, Pei & Xu, Yan & Wang, Jianzhou, 2024. "A robust time-varying weight combined model for crude oil price forecasting," Energy, Elsevier, vol. 299(C).
    3. Wang, Ning & Guo, Ziyu & Shang, Dawei & Li, Keyuyang, 2024. "Carbon trading price forecasting in digitalization social change era using an explainable machine learning approach: The case of China as emerging country evidence," Technological Forecasting and Social Change, Elsevier, vol. 200(C).

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