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基于集体实证模式分解和机器学习算法的中国碳价格预测
Qiuju Yu1,2, Rosmanjawati Abdul Rahman3, Yimin Wu1,2
1School of Mathematical Sciences, Universiti Sains Malaysia, 11800, Penang, Malaysia.
Environmental science and pollution research international
|October 21, 2024
概括
准确的碳价格预测对于减排和市场发展至关重要. 本研究使用混合机器学习模型,如GA-BP和PSO-LSSVM,以改善中国的短期碳价格预测.
科学领域:
- 环境经济学环境经济学
- 机器学习 机器学习
- 金融预测 金融预测
背景情况:
- 碳排放交易市场对于环境保护和节能至关重要.
- 准确的碳价格预测支持中国参与国际碳融资和试点市场发展.
- 碳价格信号往往是非静止的,这对传统的预测方法构成了挑战.
研究的目的:
- 开发和评估混合机器学习模型,用于准确的短期碳价格预测.
- 提高中国试点市场碳价格预测准确度.
- 探索非静态金融时间序列的先进分解和优化技术.
主要方法:
- 综合实证模式分解 (EEDM) 用于将非静态碳价格数据分解为内在模式函数 (IMF) 和余量.
- 开发了一种混合基因算法 (GA) 和逆向传播 (BP) 神经网络模型,用于短期价格预测,克服本地优化问题.
- 采用混合最小平方支持向量机 (LSSVM) 和粒子集群优化 (PSO) 模型来最大限度地减少预测错误和搜索参数.
主要成果:
- 与其他算法相比,混合GA-BP模型在短期碳价格预测方面表现优越.
- 混合PSO-LSSVM模型有效地减少了预测错误和优化了搜索参数,优于传统的神经网络方法.
- 经验分析的重点是广东,湖北和深,这是中国的关键试点碳市场.
结论:
- 综合分解和优化技术的混合机器学习模型在碳价格预测准确性方面取得了显著的改进.
- 提出的方法为中国正在发展的碳市场的政策制定者和市场参与者提供了有价值的工具.
- 先进的预测技术对于有效的碳排放交易和环境政策实施至关重要.
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