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<p>The carbon trading market is directed by policy and responsive to a multitude of factors, experiencing considerable price volatility that mirrors the supply–demand dynamics of greenhouse gas emissions rights and the economic expenses of carbon reduction measures. Improving prediction accuracy of carbon prices is crucial for investors and policymakers. It can help investors avoid risks and provide a basis for policymakers to formulate effective policies. The study employed a lightweight hybrid model to forecast carbon prices. Firstly, Marine Predators Algorithm Optimized Variational Mode Decomposition (MPA-VMD) was applied to decompose the original carbon price time series to obtain optimal parameters and subsequences. Secondly, the Minimal Gated Memory Network (MGM), a simplified network structure, was proposed to forecasting subsequences, reducing training time without compromising prediction accuracy. Thirdly, the MGM was integrated with quantile regression (QR) to predict the conditional quantiles of each subsequence. Fourthly, the probability density function was estimated based on the conditional quantiles of the carbon price using the Kernel Density Estimation (KDE) method. Finally, the final forecasting values of point prediction, interval prediction and comprehensive probability were obtained through the linear superposition of each subsequence, respectively. Experimental results showed that the performance of the proposed model was validated across five aspects: the superiority of the decomposition method, point prediction accuracy, suitable prediction interval, comprehensive probability prediction performance, and training time relative to six benchmark models.</p>

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A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月3日

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  • Dabin Zhang,
  • Yufeng Ye,
  • Yongmei Fang,
  • Jing Zhou

摘要

The carbon trading market is directed by policy and responsive to a multitude of factors, experiencing considerable price volatility that mirrors the supply–demand dynamics of greenhouse gas emissions rights and the economic expenses of carbon reduction measures. Improving prediction accuracy of carbon prices is crucial for investors and policymakers. It can help investors avoid risks and provide a basis for policymakers to formulate effective policies. The study employed a lightweight hybrid model to forecast carbon prices. Firstly, Marine Predators Algorithm Optimized Variational Mode Decomposition (MPA-VMD) was applied to decompose the original carbon price time series to obtain optimal parameters and subsequences. Secondly, the Minimal Gated Memory Network (MGM), a simplified network structure, was proposed to forecasting subsequences, reducing training time without compromising prediction accuracy. Thirdly, the MGM was integrated with quantile regression (QR) to predict the conditional quantiles of each subsequence. Fourthly, the probability density function was estimated based on the conditional quantiles of the carbon price using the Kernel Density Estimation (KDE) method. Finally, the final forecasting values of point prediction, interval prediction and comprehensive probability were obtained through the linear superposition of each subsequence, respectively. Experimental results showed that the performance of the proposed model was validated across five aspects: the superiority of the decomposition method, point prediction accuracy, suitable prediction interval, comprehensive probability prediction performance, and training time relative to six benchmark models.