Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach
Numerous studies show that it is reasonable and effective to apply decomposition technology to deal with the complex carbon price series. However, the existing research ignores the residual term containing complex information after applying single decomposition technique. Considering the demand for...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2022.991570/full |
| _version_ | 1797960165424627712 |
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| author | Tingting Zhang Zhenpeng Tang |
| author_facet | Tingting Zhang Zhenpeng Tang |
| author_sort | Tingting Zhang |
| collection | DOAJ |
| description | Numerous studies show that it is reasonable and effective to apply decomposition technology to deal with the complex carbon price series. However, the existing research ignores the residual term containing complex information after applying single decomposition technique. Considering the demand for higher accuracy of the carbon price series prediction and following the existing research path, this paper proposes a new hybrid prediction model VMD-CEEMDAN-LSSVM-LSTM, which combines a new quadratic decomposition technique with the optimized long short term memory (LSTM). In the decomposition part of the hybrid model, the original carbon price series is processed by variational mode decomposition (VMD), and then the residual term obtained by decomposition is further decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the prediction part of the hybrid model, least squares support vector machine (LSSVM) is introduced, and LSSVM-LSTM model is constructed to predict the components obtained by decomposition. The empirical research of this paper selects two different case data from the European Union emissions trading system (EU ETS) as samples. Taking the results of Case Ⅰ in the 1-step ahead forecasting scenario as an example, the prediction evaluation indexes eMAPE, eRMSE and R2 of the VMD-CEEMDAN-LSSVM-LSTM hybrid model constructed in this paper are 0.3087, 0.0921 and 0.9987 respectively, which are significantly better than other benchmark models. The empirical results confirm the superiority and robustness of the hybrid model proposed in this paper for carbon price forecasting. |
| first_indexed | 2024-04-11T00:42:03Z |
| format | Article |
| id | doaj.art-fdf7a44212bc426581e01192e95e42dd |
| institution | Directory Open Access Journal |
| issn | 2296-598X |
| language | English |
| last_indexed | 2024-04-11T00:42:03Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj.art-fdf7a44212bc426581e01192e95e42dd2023-01-06T05:35:00ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2023-01-011010.3389/fenrg.2022.991570991570Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approachTingting ZhangZhenpeng TangNumerous studies show that it is reasonable and effective to apply decomposition technology to deal with the complex carbon price series. However, the existing research ignores the residual term containing complex information after applying single decomposition technique. Considering the demand for higher accuracy of the carbon price series prediction and following the existing research path, this paper proposes a new hybrid prediction model VMD-CEEMDAN-LSSVM-LSTM, which combines a new quadratic decomposition technique with the optimized long short term memory (LSTM). In the decomposition part of the hybrid model, the original carbon price series is processed by variational mode decomposition (VMD), and then the residual term obtained by decomposition is further decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). In the prediction part of the hybrid model, least squares support vector machine (LSSVM) is introduced, and LSSVM-LSTM model is constructed to predict the components obtained by decomposition. The empirical research of this paper selects two different case data from the European Union emissions trading system (EU ETS) as samples. Taking the results of Case Ⅰ in the 1-step ahead forecasting scenario as an example, the prediction evaluation indexes eMAPE, eRMSE and R2 of the VMD-CEEMDAN-LSSVM-LSTM hybrid model constructed in this paper are 0.3087, 0.0921 and 0.9987 respectively, which are significantly better than other benchmark models. The empirical results confirm the superiority and robustness of the hybrid model proposed in this paper for carbon price forecasting.https://www.frontiersin.org/articles/10.3389/fenrg.2022.991570/fullcarbon pricequadratic decomposition techniqueVMD-CEEMDANLSSVM-LSTMmulti-step ahead forecasting |
| spellingShingle | Tingting Zhang Zhenpeng Tang Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach Frontiers in Energy Research carbon price quadratic decomposition technique VMD-CEEMDAN LSSVM-LSTM multi-step ahead forecasting |
| title | Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach |
| title_full | Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach |
| title_fullStr | Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach |
| title_full_unstemmed | Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach |
| title_short | Multi-step carbon price forecasting based on a new quadratic decomposition ensemble learning approach |
| title_sort | multi step carbon price forecasting based on a new quadratic decomposition ensemble learning approach |
| topic | carbon price quadratic decomposition technique VMD-CEEMDAN LSSVM-LSTM multi-step ahead forecasting |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2022.991570/full |
| work_keys_str_mv | AT tingtingzhang multistepcarbonpriceforecastingbasedonanewquadraticdecompositionensemblelearningapproach AT zhenpengtang multistepcarbonpriceforecastingbasedonanewquadraticdecompositionensemblelearningapproach |