预测中国碳价格,使用错误纠正的二次分解混合模型,集成模糊分散和深度学习范式
Po Yun1, Yingtong Zhou2, Chenghui Liu2
1School of Economics and Management, Hefei University, Hefei, 230601, China. yunpo2010@mail.hfut.edu.cn.
Environmental science and pollution research international
|February 6, 2024
概括
准确的中国碳价格预测是通过一个经过错误纠正的模型实现的,该模型集成了改进的完整集合实证模式分解与自适应噪声 (ICEEMDAN) 和模糊分散 (FDE). 这种先进的深度学习方法可以提高投资和减排决策.
科学领域:
- 环境经济学环境经济学
- 计算金融是指计算金融.
- 数据科学数据科学数据科学
背景情况:
- 准确的碳价格预测对于中国的有效投资和减排战略至关重要.
- 现有的模型可能无法完全捕捉到碳定价的复杂动态.
研究的目的:
- 为中国开发和验证一个经过纠错的碳价格预测模型.
- 整合先进的信号分解和深度学习技术,以提高准确性.
主要方法:
- 改进了用于信号分解的完整集体实证模式分解与自适应噪声 (ICEEMDAN).
- 模糊分散 (FDE) 用于识别复杂的信号.
- 变化模式分解 (VMD) 和粒子群优化长期短期记忆 (PSO-LSTM) 用于预测.
- 错误纠正 (EC) 方法来改进预测.
主要成果:
- 拟议的ICEEMDAN-FDE-VMD-PSO-LSTM-EC模型显著优于初级分解模型.
- 深度学习PSO-LSTM模型在预测中国的碳价格方面表现出优越性.
- 经过错误纠正的方法有效提高了预测准确性,实现了0.0877的RMSE和0.9998.8的R值.
结论:
- 综合模型提供高度准确的碳价格预测,包括长期预测.
- 这些发现有助于理解碳价格的特征,并制定有效的市场法规.
- 这项研究证实了高级分解和深度学习对于财务时间序列预测的有效性.
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