Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting
Abstract Accurate carbon price forecasts are crucial for policymakers and enterprises to understand the dynamics of carbon price fluctuations, enabling them to formulate informed policies and investment strategies. However, due to the non-linear and non-stationary nature of carbon price, traditional...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01609-7 |
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| _version_ | 1832571143367688192 |
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| author | Wang Zhong Wang Yue Wang Haoran Tang Nan Wang Shuyue |
| author_facet | Wang Zhong Wang Yue Wang Haoran Tang Nan Wang Shuyue |
| author_sort | Wang Zhong |
| collection | DOAJ |
| description | Abstract Accurate carbon price forecasts are crucial for policymakers and enterprises to understand the dynamics of carbon price fluctuations, enabling them to formulate informed policies and investment strategies. However, due to the non-linear and non-stationary nature of carbon price, traditional models often struggle to achieve high prediction accuracy. To address this challenge, this study proposes a novel integrated prediction framework designed to enhance forecast accuracy. First, the carbon price series is decomposed into a series of smoother subsequences using fast iterative filtering (FIF). Subsequently, an integrated prediction model, AM-TCN-LSTM, is constructed, incorporating the attention mechanism (AM), temporal convolutional networks (TCN), and long short-term memory (LSTM) neural networks. The attention mechanism adaptively captures complex features from multiple factors, while the TCN-LSTM efficiently extracts temporal features from the sequences. Finally, the results from each subsequence are aggregated to generate the final prediction. Five carbon markets in china: Guangdong, Hubei, Shenzhen, Beijing, and Shanghai were selected to verify the validity of the proposed model. Various comparative models and evaluation metrics were employed to assess performance. The results demonstrate that: (1) the TCN-LSTM model achieves higher prediction accuracy compared to single models. (2) FIF is a more effective decomposition method with superior performance compared to EMD-based methods. (3) The proposed model exhibits the highest predictive capability, with MAE values of 0.0964, 0.1403, 1.9476, 2.0848, and 0.5029 for the five carbon markets, significantly outperforming comparison models. (4) The attention mechanism effectively captures the influence of multiple factors on carbon price, particularly within the short-term components. |
| format | Article |
| id | doaj-art-0c22c4eef71f48959947b577afc685d9 |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-0c22c4eef71f48959947b577afc685d92025-02-02T12:48:54ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111112110.1007/s40747-024-01609-7Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecastingWang Zhong0Wang Yue1Wang Haoran2Tang Nan3Wang Shuyue4College of Management Science, Chengdu University of TechnologyCollege of Management Science, Chengdu University of TechnologyCollege of Management Science, Chengdu University of TechnologyCollege of Management Science, Chengdu University of TechnologyThe College of Nuclear Technology and Automation Engineering, Chengdu University of TechnologyAbstract Accurate carbon price forecasts are crucial for policymakers and enterprises to understand the dynamics of carbon price fluctuations, enabling them to formulate informed policies and investment strategies. However, due to the non-linear and non-stationary nature of carbon price, traditional models often struggle to achieve high prediction accuracy. To address this challenge, this study proposes a novel integrated prediction framework designed to enhance forecast accuracy. First, the carbon price series is decomposed into a series of smoother subsequences using fast iterative filtering (FIF). Subsequently, an integrated prediction model, AM-TCN-LSTM, is constructed, incorporating the attention mechanism (AM), temporal convolutional networks (TCN), and long short-term memory (LSTM) neural networks. The attention mechanism adaptively captures complex features from multiple factors, while the TCN-LSTM efficiently extracts temporal features from the sequences. Finally, the results from each subsequence are aggregated to generate the final prediction. Five carbon markets in china: Guangdong, Hubei, Shenzhen, Beijing, and Shanghai were selected to verify the validity of the proposed model. Various comparative models and evaluation metrics were employed to assess performance. The results demonstrate that: (1) the TCN-LSTM model achieves higher prediction accuracy compared to single models. (2) FIF is a more effective decomposition method with superior performance compared to EMD-based methods. (3) The proposed model exhibits the highest predictive capability, with MAE values of 0.0964, 0.1403, 1.9476, 2.0848, and 0.5029 for the five carbon markets, significantly outperforming comparison models. (4) The attention mechanism effectively captures the influence of multiple factors on carbon price, particularly within the short-term components.https://doi.org/10.1007/s40747-024-01609-7Carbon priceFast iterative filteringTemporal convolution neural networkLong-short term memoryAttention mechanism |
| spellingShingle | Wang Zhong Wang Yue Wang Haoran Tang Nan Wang Shuyue Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting Complex & Intelligent Systems Carbon price Fast iterative filtering Temporal convolution neural network Long-short term memory Attention mechanism |
| title | Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting |
| title_full | Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting |
| title_fullStr | Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting |
| title_full_unstemmed | Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting |
| title_short | Integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting |
| title_sort | integrating fast iterative filtering and ensemble neural network structure with attention mechanism for carbon price forecasting |
| topic | Carbon price Fast iterative filtering Temporal convolution neural network Long-short term memory Attention mechanism |
| url | https://doi.org/10.1007/s40747-024-01609-7 |
| work_keys_str_mv | AT wangzhong integratingfastiterativefilteringandensembleneuralnetworkstructurewithattentionmechanismforcarbonpriceforecasting AT wangyue integratingfastiterativefilteringandensembleneuralnetworkstructurewithattentionmechanismforcarbonpriceforecasting AT wanghaoran integratingfastiterativefilteringandensembleneuralnetworkstructurewithattentionmechanismforcarbonpriceforecasting AT tangnan integratingfastiterativefilteringandensembleneuralnetworkstructurewithattentionmechanismforcarbonpriceforecasting AT wangshuyue integratingfastiterativefilteringandensembleneuralnetworkstructurewithattentionmechanismforcarbonpriceforecasting |