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Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm

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Cited by:

  1. Ding, Lili & Zhang, Rui & Zhao, Xin, 2024. "Forecasting carbon price in China unified carbon market using a novel hybrid method with three-stage algorithm and long short-term memory neural networks," Energy, Elsevier, vol. 288(C).
  2. Qing Liu & Huina Jin & Xiang Bai & Jinliang Zhang, 2023. "Prediction and Analysis of the Price of Carbon Emission Rights in Shanghai: Under the Background of COVID-19 and the Russia–Ukraine Conflict," Mathematics, MDPI, vol. 11(14), pages 1-16, July.
  3. Liao, Haolan & Wu, Di & Wang, Yuhan & Lyu, Zeyu & Sun, Hongmei & Nie, Yongyou & He, He, 2022. "Impacts of carbon trading mechanism on closed-loop supply chain: A case study of stringer pallet remanufacturing," Socio-Economic Planning Sciences, Elsevier, vol. 81(C).
  4. 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.
  5. Sun, Wei & Huang, Chenchen, 2020. "A novel carbon price prediction model combines the secondary decomposition algorithm and the long short-term memory network," Energy, Elsevier, vol. 207(C).
  6. Yin, Hao & Yin, Yiding & Li, Hanhong & Zhu, Jianbin & Xian, Zikang & Tang, Yanshu & Xiao, Liexi & Rong, Jiayu & Li, Chen & Zhang, Haitao & Xie, Zhifeng & Meng, Anbo, 2025. "Carbon emissions trading price forecasting based on temporal-spatial multidimensional collaborative attention network and segment imbalance regression," Applied Energy, Elsevier, vol. 377(PA).
  7. Xingmin Zhang & Zhiyong Li & Yiming Zhao & Lan Wang, 2025. "Carbon trading and COVID-19: a hybrid machine learning approach for international carbon price forecasting," Annals of Operations Research, Springer, vol. 345(2), pages 1267-1295, February.
  8. Jujie Wang & Maolin He, 2025. "Extended decomposition ensemble framework based on full data analysis and optimized combination with relaxed boundary for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(1), pages 909-942, January.
  9. Shanglei Chai & Zixuan Zhang & Zhen Zhang, 2025. "Carbon price prediction for China's ETS pilots using variational mode decomposition and optimized extreme learning machine," Annals of Operations Research, Springer, vol. 345(2), pages 809-830, February.
  10. Lei, Heng & Xue, Minggao & Liu, Huiling, 2022. "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, vol. 113(C).
  11. Niu, Xinsong & Wang, Jiyang & Wei, Danxiang & Zhang, Lifang, 2022. "A combined forecasting framework including point prediction and interval prediction for carbon emission trading prices," Renewable Energy, Elsevier, vol. 201(P1), pages 46-59.
  12. Horobet, Alexandra & Boubaker, Sabri & Belascu, Lucian & Negreanu, Cristina Carmencita & Dinca, Zeno, 2024. "Technology-driven advancements: Mapping the landscape of algorithmic trading literature," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
  13. Zhang, Tingting & Tang, Zhenpeng & Wu, Junchuan & Du, Xiaoxu & Chen, Kaijie, 2021. "Multi-step-ahead crude oil price forecasting based on two-layer decomposition technique and extreme learning machine optimized by the particle swarm optimization algorithm," Energy, Elsevier, vol. 229(C).
  14. Sun, Wei & Zhang, Junjian, 2022. "A novel carbon price prediction model based on optimized least square support vector machine combining characteristic-scale decomposition and phase space reconstruction," Energy, Elsevier, vol. 253(C).
  15. Zhang, Chu & Ji, Chunlei & Hua, Lei & Ma, Huixin & Nazir, Muhammad Shahzad & Peng, Tian, 2022. "Evolutionary quantile regression gated recurrent unit network based on variational mode decomposition, improved whale optimization algorithm for probabilistic short-term wind speed prediction," Renewable Energy, Elsevier, vol. 197(C), pages 668-682.
  16. Taiyong Li & Yingrui Zhou & Xinsheng Li & Jiang Wu & Ting He, 2019. "Forecasting Daily Crude Oil Prices Using Improved CEEMDAN and Ridge Regression-Based Predictors," Energies, MDPI, vol. 12(19), pages 1-25, September.
  17. 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).
  18. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  19. Cai, Yi & Tang, Zhenpeng & Chen, Ying, 2024. "Can real-time investor sentiment help predict the high-frequency stock returns? Evidence from a mixed-frequency-rolling decomposition forecasting method," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).
  20. 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).
  21. Lunyou Pei & Bing Wang & Ying Liu & Xiaoling Liu, 2023. "Optimization of Preventive Maintenance Timing of Highway Bridges Considering China’s “Dual Carbon” Target," Sustainability, MDPI, vol. 15(23), pages 1-15, November.
  22. Chun-Yao Lee & Guang-Lin Zhuo, 2021. "A Hybrid Whale Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 9(13), pages 1-19, June.
  23. Huang, Zhehao & Dong, Hao & Jia, Shuaishuai, 2022. "Equilibrium pricing for carbon emission in response to the target of carbon emission peaking," Energy Economics, Elsevier, vol. 112(C).
  24. Li, Guohui & Ning, Zhiyuan & Yang, Hong & Gao, Lipeng, 2022. "A new carbon price prediction model," Energy, Elsevier, vol. 239(PD).
  25. Jianguo Zhou & Dongfeng Chen, 2021. "Carbon Price Forecasting Based on Improved CEEMDAN and Extreme Learning Machine Optimized by Sparrow Search Algorithm," Sustainability, MDPI, vol. 13(9), pages 1-20, April.
  26. Xu, Yingying & Dai, Yifan & Guo, Lingling & Chen, Jingjing, 2024. "Leveraging machine learning to forecast carbon returns: Factors from energy markets," Applied Energy, Elsevier, vol. 357(C).
  27. Tang, Ling & Wang, Haohan & Li, Ling & Yang, Kaitong & Mi, Zhifu, 2020. "Quantitative models in emission trading system research: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
  28. Jianguo Zhou & Qiqi Wang, 2021. "Forecasting Carbon Price with Secondary Decomposition Algorithm and Optimized Extreme Learning Machine," Sustainability, MDPI, vol. 13(15), pages 1-17, July.
  29. Zhang, Meng & Kang, Guoqing & Wu, Lifeng & Guan, Yong, 2022. "A method for capacity prediction of lithium-ion batteries under small sample conditions," Energy, Elsevier, vol. 238(PC).
  30. 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).
  31. 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).
  32. Alexandra Horobet & Sabri Boubaker & Lucian Belascu & Cristina Carmencita Negreanu & Zeno Dinca, 2024. "Technology-driven advancements: Mapping the landscape of algorithmic trading literature," Post-Print hal-04990283, HAL.
  33. Zhou, Feite & Huang, Zhehao & Zhang, Changhong, 2022. "Carbon price forecasting based on CEEMDAN and LSTM," Applied Energy, Elsevier, vol. 311(C).
  34. Wu, Qunli & Ma, Zhe & Meng, Fanxing, 2022. "Long-term impacts of carbon allowance allocation in China: An IC-DCGE model optimized by the hypothesis of imperfectly competitive market," Energy, Elsevier, vol. 241(C).
  35. Sun, Qingqing & Chen, Hong & Long, Ruyin & Chen, Jiawei, 2024. "Integrated prediction of carbon price in China based on heterogeneous structural information and wall-value constraints," Energy, Elsevier, vol. 306(C).
  36. Li, Jingmiao & Liu, Dehong, 2023. "Carbon price forecasting based on secondary decomposition and feature screening," Energy, Elsevier, vol. 278(PA).
  37. Jianguo Zhou & Xuejing Huo & Xiaolei Xu & Yushuo Li, 2019. "Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm," Energies, MDPI, vol. 12(5), pages 1-22, March.
  38. Yongjie Zhang & Yue Li & Dehua Shen, 2022. "Investor Attention and the Carbon Emission Markets in China: A Nonparametric Wavelet-Based Causality Test," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 29(1), pages 123-137, March.
  39. Song, Chao & Wang, Tao & Chen, Xiaohong & Shao, Quanxi & Zhang, Xianqi, 2023. "Ensemble framework for daily carbon dioxide emissions forecasting based on the signal decomposition–reconstruction model," Applied Energy, Elsevier, vol. 345(C).
  40. 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).
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