Carbon Price Point–Interval Forecasting Based on Two-Layer Decomposition and Deep Learning Combined Model Using Weight Assignment DOI
Xiwen Cui, Dongxiao Niu

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: unknown, P. 144124 - 144124

Published: Oct. 1, 2024

Language: Английский

A Decomposition-Integration Framework of Carbon Price Forecasting Based on Econometrics and Machine Learning Methods DOI Creative Commons
Zhehao Huang,

Benhuan Nie,

Yuqiao Lan

et al.

Mathematics, Journal Year: 2025, Volume and Issue: 13(3), P. 464 - 464

Published: Jan. 30, 2025

Carbon price forecasting and pricing are critical for stabilizing carbon markets, mitigating investment risks, fostering economic development. This paper presents an advanced decomposition-integration framework which seamlessly integrates econometric models with machine learning techniques to enhance forecasting. First, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) method is employed decompose data into distinct modal components, each defined by specific frequency characteristics. Then, Lempel–Ziv complexity dispersion entropy algorithms applied analyze these facilitating identification of their unique attributes. The subsequently employs GARCH predicting high-frequency components a gated recurrent unit (GRU) neural network optimized grey wolf algorithm low-frequency components. Finally, GRU model utilized integrate predictive outcomes nonlinearly, ensuring comprehensive precise forecast. Empirical evidence demonstrates that this not only accurately captures diverse characteristics different but also significantly outperforms traditional benchmark in accuracy. By optimizing optimizer (GWO) algorithm, enhances both prediction stability adaptability, while nonlinear integration approach effectively mitigates error accumulation. innovative offers scientifically rigorous efficient tool forecasting, providing valuable insights policymakers market participants trading.

Language: Английский

Citations

0
MLP-Carbon: A new paradigm integrating multi-frequency and multi-scale techniques for accurate carbon price forecasting DOI
Zhirui Tian, Wei Sun, Chenye Wu

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 383, P. 125330 - 125330

Published: Jan. 15, 2025

Language: Английский

Citations

0
A hybrid model for carbon price forecasting based on SSA-NSTransformer: Considering the role of multi-stage carbon reduction targets DOI
Jinchao Li, Yuwei Guo

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124237 - 124237

Published: Jan. 29, 2025

Language: Английский

Citations

0
Technological innovations fuel carbon prices and transform environmental management across Europe DOI Creative Commons
Mehmet Balcılar, Ahmed H. Elsayed, Rabeh Khalfaoui

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 373, P. 123663 - 123663

Published: Dec. 17, 2024

Language: Английский

Citations

1
Carbon Price Point–Interval Forecasting Based on Two-Layer Decomposition and Deep Learning Combined Model Using Weight Assignment DOI
Xiwen Cui, Dongxiao Niu

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: unknown, P. 144124 - 144124

Published: Oct. 1, 2024

Language: Английский

Citations

0