基于新型混合机器学习预测碳期货价格:不同时期碳价格的比较研究
Xi Zhang1, Kailing Yang1, Qin Lu2
1School of Business, Chengdu University of Technology, Chengdu, 610059, China.
Journal of environmental management
|September 15, 2023
概括
准确的碳价格预测对于能源安全至关重要. 一个新的VMD-CNN-BILSTM-MLP模型有效地预测了欧元碳期货价格,优于现有的方法.
科学领域:
- 环境经济学环境经济学
- 计算金融是指计算金融.
- 时间序列分析时间序列分析
背景情况:
- 准确的碳价格预测对于有效的气候政策和国家能源安全至关重要.
- 了解碳市场动态有助于制定强有力的减排战略.
- 现有的预测模型经常与碳期货价格的复杂性和波动性作斗争.
研究的目的:
- 开发和验证一种新的混合模型,用于预测欧盟碳配额 (EUA) 碳期货价格.
- 评估模型在不同时期与减排政策相比的表现.
- 证明拟议模型在传统预测技术上的优越性.
主要方法:
- 这是一个混合模型,集成了变化模式分解 (VMD),卷积神经网络 (CNN),双向长期短期记忆 (BILSTM) 和多层感知器 (MLP).
- 由遗传算法 (GA) 优化的VMD将碳期货价格分解为不同的频率次序.
- 使用CNN-BILSTM和MLP来预测不同的频次次序,并将结果结合起来进行最终预测.
主要成果:
- 与其他模型相比,VMD-CNN-BILSTM-MLP模型在政策前和政策后的两个时期都显示出更高的预测准确性.
- 绩效使用RMSE,MAE,MAPE,R2和MDM等指标进行评估.
- 该模型在政策引入前的五年中,在预测价格方面表现稍好一些.
结论:
- 拟议的VDD-CNN-BILSTM-MLP混合模型为预测碳期货价格提供了一个强大而有效的方法.
- 证实了该模型在不同市场条件和政策环境中的适应性.
- 这项研究为政策制定者和市场参与者在导航碳市场方面提供了有价值的工具.
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