A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction
Problem
Carbon trading prices are nonlinear and driven by feature factors arriving at inconsistent frequencies, making accurate prediction difficult for market participants and regulators.
Why it matters
Accurate carbon price forecasts support effective emissions management and sustainable development goals.
What the AI did
A multifrequency data fusion model (MFF-CPPM) combining a feature-extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer was used to predict carbon prices.
Results
Outperformed baseline models in prediction accuracy and trend forecasting in Guangdong, with robustness confirmed in Hubei and Beijing; no numeric metrics stated.
Impact
The evidence supports better carbon price forecasting in three Chinese pilot markets, but no quantitative gains are given.
Record
| Venue | Journal of Forecasting |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Canran Xiao, Yongmei Liu |
| Citations | 22 (OpenAlex, at import time) |
| References | 87 |
| Model or method | MFF-CPPM (multifrequency data fusion transformer) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1002/for.3198 |
| Open access | bronze |
| Keywords | carbon price prediction, deep learning, transformer, data fusion, forecasting |
Importance rating
real but narrower advance
Carbon price forecasting supports emissions management and carbon markets; a real but narrower market-prediction advance tested in three Chinese pilot markets.
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