一个基于双变量实证模式分解和错误纠正的两阶段间隔估值碳价格预测模型
Piao Wang1, Muhammad Adnan Zahid Chudhery2, Jilan Xu3
1School of Big Data and Statistics, Anhui University, Hefei, 230601, China.
概括
准确的碳价格预测对于碳市场至关重要. 本研究引入了一种新的两阶段模型,使用双变实证模式分解 (BEMD) 和神经网络来改进间隔值碳价格预测.
科学领域:
- 环境经济学环境经济学
- 气候变化政策 气候变化政策
- 数据科学数据科学数据科学
背景情况:
- 全球经济发展增加了温室气体排放,推动了气候变化.
- 准确的碳价格预测对于有效的碳定价和市场稳定至关重要.
- 现有的预测模型可能无法完全捕捉到碳价格动态的复杂性.
研究的目的:
- 提出和验证一个新的两阶段间隔值碳价格组合预测模型.
- 提高碳价格预测的准确性和稳定性.
- 为政策制定者和碳市场投资者提供一个强大的工具.
主要方法:
- 两变实证模式分解 (BEMD) 用于将碳价格和影响因素分解为间隔子模式.
- 在第一阶段使用人工智能 (AI) 神经网络 (IMLP,LSTM,GRU,CNN) 进行组合预测.
- 在第二阶段使用长短期内存 (LSTM) 网络进行错误纠正,以改进预测.
主要成果:
- 拟议的两阶段模型显著优于区间估值碳价格的单一预测方法.
- 间隔子模式的I阶段组合预测显示出卓越的性能.
- 第二阶段的错误纠正进一步提高了预测的准确性和稳定性.
结论:
- 开发的模型是一种有效的方法,用于区间估值碳价格预测.
- 该模型有助于决策者制定减排战略.
- 它帮助投资者减轻碳市场中的风险.
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