A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting
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ML can be used to analyze prices within cap-and-trade markets, for example by predicting prices via supervised learning or analyzing the main drivers of prices via hierarchical clustering.
“ML can be used to analyze prices within these markets, for example by predicting prices via supervised learning [769,834,890,892] or analyzing the main drivers of prices via hierarchical clustering [891].”
From Tackling climate change with machine learning (2023) · cites this paper as [892]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- Zhu, B., Ye, S., Wang, P., He, K., Zhang, T., & Wei, Y.-M.
- Affiliations
- Jinan University (CN)
- Hunan University of Science and Technology (CN)
- University of Birmingham (GB)
- Beijing Institute of Technology (CN)
- Corresponding author
- Zhu, Bangzhu
- He, Kaijian
- Wei, Yi-Ming
- Venue
- Energy Economics
- Publisher
- Elsevier BV
- Published
- 2018-02
- Volume
- 70
- Pages
- 143-157
- ISSN
- 0140-9883 (ISSN-L)
- OpenAlex
- W2781854549
- Cited by
- 236
- References
- 34
- Citations by year
- 2026: 22 · 2025: 36 · 2024: 26 · 2023: 38 · 2022: 43 · 2021: 27 · 2020: 16 · 2019: 20 · 2018: 8
- FWCI
- 14.15
- Type
- journal article
- Language
- en
- Open access
- green
- dspace.lboro.ac.uk
- Licence
- cc-by-nc-nd
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Locations
- Energy Economics (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Loughborough University Institutional Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- Loughborough University Research Repository (Loughborough University) (submittedVersion, cc-by-nc-nd)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion, cc-by-nc-nd)
- Funders
- National Natural Science Foundation of China (71771105, 71473180, 71303174)
- National Philosophy and Social Science Foundation of China (15ZDA054, 16ZZD049)
- Natural Science Foundation for Distinguished Young Talents of Guangdong (2014A030306031)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Keywords
- carbon price forecasting, empirical mode decomposition, least squares support vector machine, ensemble forecasting, Lempel-Ziv complexity, GARCH models, particle swarm optimization, intrinsic mode functions, non-stationary time series, directional prediction
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