A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration
The complex features of carbon price, such as volatility and nonlinearity, pose a serious challenge to accurately predict it. To this end, this paper proposes a novel forecasting framework for carbon emission trading price based on nonlinear integration, including feature selection, deep learning an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/10/1624 |
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| author | Rulin Gao Jingyun Sun |
| author_facet | Rulin Gao Jingyun Sun |
| author_sort | Rulin Gao |
| collection | DOAJ |
| description | The complex features of carbon price, such as volatility and nonlinearity, pose a serious challenge to accurately predict it. To this end, this paper proposes a novel forecasting framework for carbon emission trading price based on nonlinear integration, including feature selection, deep learning and model combination. Firstly, the historical carbon price series are collected and collated, and the factors affecting the carbon price are analyzed. Secondly, the data are downscaled and the input variables are screened using the max-relevance and min-redundancy. Then, the three integrated learning models are combined with the neural network model through nonlinear integration to construct a hybrid prediction model, and the best performing combined model is obtained. Finally, interval prediction is realized on the basis of point prediction. The experimental results show that the prediction model outperforms other comparative models in terms of prediction accuracy, stability and statistical hypothesis testing, and has good prediction performance. In summary, the hybrid prediction model proposed in this paper can not only provide high-precision carbon market price prediction for government and enterprise decision makers, but also help investors optimize their trading strategies and improve their returns. |
| format | Article |
| id | doaj-art-85ec93b8fe7d47ee971431e7be3e2263 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-85ec93b8fe7d47ee971431e7be3e22632025-08-20T03:47:57ZengMDPI AGMathematics2227-73902025-05-011310162410.3390/math13101624A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear IntegrationRulin Gao0Jingyun Sun1School of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, ChinaSchool of Statistics and Data Science, Lanzhou University of Finance and Economics, Lanzhou 730020, ChinaThe complex features of carbon price, such as volatility and nonlinearity, pose a serious challenge to accurately predict it. To this end, this paper proposes a novel forecasting framework for carbon emission trading price based on nonlinear integration, including feature selection, deep learning and model combination. Firstly, the historical carbon price series are collected and collated, and the factors affecting the carbon price are analyzed. Secondly, the data are downscaled and the input variables are screened using the max-relevance and min-redundancy. Then, the three integrated learning models are combined with the neural network model through nonlinear integration to construct a hybrid prediction model, and the best performing combined model is obtained. Finally, interval prediction is realized on the basis of point prediction. The experimental results show that the prediction model outperforms other comparative models in terms of prediction accuracy, stability and statistical hypothesis testing, and has good prediction performance. In summary, the hybrid prediction model proposed in this paper can not only provide high-precision carbon market price prediction for government and enterprise decision makers, but also help investors optimize their trading strategies and improve their returns.https://www.mdpi.com/2227-7390/13/10/1624carbon price predictionhybrid forecasting modeldeep learningnonlinear ensemble |
| spellingShingle | Rulin Gao Jingyun Sun A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration Mathematics carbon price prediction hybrid forecasting model deep learning nonlinear ensemble |
| title | A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration |
| title_full | A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration |
| title_fullStr | A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration |
| title_full_unstemmed | A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration |
| title_short | A Novel Forecasting Framework for Carbon Emission Trading Price Based on Nonlinear Integration |
| title_sort | novel forecasting framework for carbon emission trading price based on nonlinear integration |
| topic | carbon price prediction hybrid forecasting model deep learning nonlinear ensemble |
| url | https://www.mdpi.com/2227-7390/13/10/1624 |
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