A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction
Abstract 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 accura...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Springer
2025-07-01
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| Series: | Management System Engineering |
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| Online Access: | https://doi.org/10.1007/s44176-025-00045-2 |
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| _version_ | 1849334545496670208 |
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| author | Dabin Zhang Yufeng Ye Yongmei Fang Jing Zhou |
| author_facet | Dabin Zhang Yufeng Ye Yongmei Fang Jing Zhou |
| author_sort | Dabin Zhang |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-7db710dfb1bf4b26866a1d95c33f70ae |
| institution | Kabale University |
| issn | 2731-5843 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Springer |
| record_format | Article |
| series | Management System Engineering |
| spelling | doaj-art-7db710dfb1bf4b26866a1d95c33f70ae2025-08-20T03:45:32ZengSpringerManagement System Engineering2731-58432025-07-014112010.1007/s44176-025-00045-2A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price predictionDabin Zhang0Yufeng Ye1Yongmei Fang2Jing Zhou3College of Mathematics and Information, South China Agricultural UniversityCollege of Mathematics and Information, South China Agricultural UniversityCollege of Mathematics and Information, South China Agricultural UniversityCollege of Mathematics and Information, South China Agricultural UniversityAbstract 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.https://doi.org/10.1007/s44176-025-00045-2Carbon price predictionQuantile regressionMinimal Gated Memory NetworkUncertainty predictionLightweight hybrid model |
| spellingShingle | Dabin Zhang Yufeng Ye Yongmei Fang Jing Zhou A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction Management System Engineering Carbon price prediction Quantile regression Minimal Gated Memory Network Uncertainty prediction Lightweight hybrid model |
| title | A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction |
| title_full | A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction |
| title_fullStr | A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction |
| title_full_unstemmed | A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction |
| title_short | A novel hybrid model based on MPA-VMD, QRMGM and KDE for carbon price prediction |
| title_sort | novel hybrid model based on mpa vmd qrmgm and kde for carbon price prediction |
| topic | Carbon price prediction Quantile regression Minimal Gated Memory Network Uncertainty prediction Lightweight hybrid model |
| url | https://doi.org/10.1007/s44176-025-00045-2 |
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