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Carbon price forecasting with complex network and extreme learning machine

Citations

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Cited by:

  1. Liu, Jinpei & Zhao, Xiaoman & Luo, Rui & Tao, Zhifu, 2024. "A novel link prediction model for interval-valued crude oil prices based on complex network and multi-source information," Applied Energy, Elsevier, vol. 376(PB).
  2. Tianqi Pang & Kehui Tan & Chenyou Fan, 2023. "Carbon Price Forecasting with Quantile Regression and Feature Selection," Papers 2305.03224, arXiv.org.
  3. Sha Liu & Yiting Zhang & Junping Wang & Danlei Feng, 2024. "Fluctuations and Forecasting of Carbon Price Based on A Hybrid Ensemble Learning GARCH-LSTM-Based Approach: A Case of Five Carbon Trading Markets in China," Sustainability, MDPI, vol. 16(4), pages 1-23, February.
  4. Shang, Dawei & Pang, Yudan & Wang, Haijie, 2025. "Carbon price fluctuation prediction using a novel hybrid statistics and machine learning approach," Energy, Elsevier, vol. 324(C).
  5. Cao, Jin-Hui & Xie, Chi & Zhou, Yang & Wang, Gang-Jin & Zhu, You, 2025. "Forecasting carbon price: A novel multi-factor spatial-temporal GNN framework integrating Graph WaveNet and self-attention mechanism," Energy Economics, Elsevier, vol. 144(C).
  6. Huang, Wenyang & Zhao, Jianyu & Wang, Xiaokang, 2024. "Model-driven multimodal LSTM-CNN for unbiased structural forecasting of European Union allowances open-high-low-close price," Energy Economics, Elsevier, vol. 132(C).
  7. Indre Siksnelyte-Butkiene & Dalia Streimikiene & Tomas Balezentis & Tomas Karpavicius, 2026. "Energy policy and climate change mitigation at national level in the European Union: A case study of Lithuania," Energy & Environment, , vol. 37(2), pages 656-680, March.
  8. Zhou, Yang & Xie, Chi & Wang, Gang-Jin & Zhu, You & Uddin, Gazi Salah, 2023. "Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning," Research in International Business and Finance, Elsevier, vol. 64(C).
  9. Cláudia R. R. Eirado & Douglas Silveira & Daniel O. Cajueiro, 2025. "Digital Twins and Network Resilience in the EU ETS: Analysing Structural Shifts in Carbon Trading," Sustainability, MDPI, vol. 17(15), pages 1-31, July.
  10. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
  11. Bangzhu Zhu & Chunzhuo Wan & Ping Wang & Julien Chevallier, 2025. "Interval Forecasting of Carbon Price With a Novel Hybrid Multiscale Decomposition and Bootstrap Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 376-390, March.
  12. Wang, Xuerui & Wang, Lin & An, Wuyue, 2024. "Probability density prediction for carbon allowance prices based on TS2Vec and distribution Transformer," Energy Economics, Elsevier, vol. 140(C).
  13. Zhang, Fang & Xia, Yan, 2022. "Carbon price prediction models based on online news information analytics," Finance Research Letters, Elsevier, vol. 46(PA).
  14. Zhang, Xin & Wang, Jujie & He, Xuecheng, 2025. "An optimal multi-scale ensemble transformer for carbon emission allowance price prediction based on time series patching and two-stage stabilization," Energy, Elsevier, vol. 328(C).
  15. Zhuolin Wu & Jiaqi Zhou & Xiaobing Yu, 2025. "Forecast Natural Gas Price by an Extreme Learning Machine Framework Based on Multi-Strategy Grey Wolf Optimizer and Signal Decomposition," Sustainability, MDPI, vol. 17(12), pages 1-37, June.
  16. Cai, Xiaotong & Yuan, Bo & Wu, Chao, 2025. "An integrated CEEMDAN and TCN-LSTM deep learning framework for forecasting," International Review of Financial Analysis, Elsevier, vol. 98(C).
  17. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
  18. Xu, Yuhong & Zhao, Xinyao, 2024. "How does node centrality in a financial network affect asset price prediction?," The North American Journal of Economics and Finance, Elsevier, vol. 73(C).
  19. Qi, Shaozhou & Cheng, Shihan & Tan, Xiujie & Feng, Shenghao & Zhou, Qi, 2022. "Predicting China's carbon price based on a multi-scale integrated model," Applied Energy, Elsevier, vol. 324(C).
  20. Ma, Changxi & Zhao, Mingxi & Huang, Xiaoting & Zhao, Yongpeng, 2024. "Optimized deep extreme learning machine for traffic prediction and autonomous vehicle lane change decision-making," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
  21. Huang, Wenyang & Wang, Huiwen & Wei, Yigang, 2023. "Identifying the determinants of European carbon allowances prices: A novel robust partial least squares method for open-high-low-close data," International Review of Financial Analysis, Elsevier, vol. 90(C).
  22. Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
  23. Sun Meng & Yan Chen, 2023. "Market Volatility Spillover, Network Diffusion, and Financial Systemic Risk Management: Financial Modeling and Empirical Study," Mathematics, MDPI, vol. 11(6), pages 1-16, March.
  24. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
  25. Dinggao Liu & Liuqing Wang & Shuo Lin & Zhenpeng Tang, 2025. "A Novel Multi-Task Learning Framework for Interval-Valued Carbon Price Forecasting Using Online News and Search Engine Data," Mathematics, MDPI, vol. 13(3), pages 1-23, January.
  26. Zhu, Mengrui & Xu, Hua & Wang, Minggang & Tian, Lixin, 2024. "Carbon price interval prediction method based on probability density recurrence network and interval multi-layer perceptron," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 636(C).
  27. Huang, Yumeng & Dai, Xingyu & Wang, Qunwei & Zhou, Dequn, 2021. "A hybrid model for carbon price forecastingusing GARCH and long short-term memory network," Applied Energy, Elsevier, vol. 285(C).
  28. Nader Trabelsi & Aviral Kumar Tiwari, 2023. "CO2 Emission Allowances Risk Prediction with GAS and GARCH Models," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 775-805, February.
  29. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
  30. Xiangjun Cai & Dagang Li & Li Feng, 2024. "Enhanced Carbon Price Forecasting Using Extended Sliding Window Decomposition with LSTM and SVR," Mathematics, MDPI, vol. 12(23), pages 1-20, November.
  31. Jesús Molina‐Muñoz & Andrés Mora‐Valencia & Javier Perote, 2024. "Predicting carbon and oil price returns using hybrid models based on machine and deep learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
  32. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2022. "Forecasting European carbon returns using dimension reduction techniques: Commodity versus financial fundamentals," International Journal of Forecasting, Elsevier, vol. 38(3), pages 944-969.
  33. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.
  34. Zhang, Wen & Wu, Zhibin & Zeng, Xiaojun & Zhu, Changhui, 2023. "An ensemble dynamic self-learning model for multiscale carbon price forecasting," Energy, Elsevier, vol. 263(PC).
  35. Wang, Minggang & Zhu, Mengrui & Tian, Lixin, 2022. "A novel framework for carbon price forecasting with uncertainties," Energy Economics, Elsevier, vol. 112(C).
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