基于多因素的HKELM,通过结合二次分解和集体学习来预测中国的区域碳价格
1School of Economics and Management, Anhui University of Science and Technology, Huainan, China.
PloS one
|December 12, 2023
概括
本研究引入了一种用于准确预测碳价格的新型混合模型,该模型结合了先进的分解技术和机器学习. 该模型显著提高了中国碳市场的预测准确性.
科学领域:
- 环境经济学环境经济学
- 金融市场 金融市场
- 计算智能是一种计算智能.
背景情况:
- 准确的碳价格预测对于在不断变化的碳金融市场中管理风险至关重要.
- 区域碳价格表现出复杂的特征,需要复杂的预测方法.
- 现有的模型往往难以捕捉碳定价的复杂动态.
研究的目的:
- 开发和验证一个可靠的模型来预测中国的碳价格.
- 通过整合多因素分析和先进的机器学习来提高碳价格预测的准确性.
- 在复杂的碳市场数据背景下,解决当前预测方法的局限性.
主要方法:
- 利用变化模式分解 (VMD) 来进行信号分解和范围来进行模式重建.
- 采用了基于多因素混合内核的极端学习机器 (HKELM),通过子搜索算法进行优化.
- 集成的最大信息系数用于选择关键外部因素以及历史价格数据作为输入.
- 应用了改进的完整整合体实证模式分解与适应性噪声和范围到剩余项.
- 集成非线性集体学习,用于最终预测精细化.
主要成果:
- 拟议的HKELM模型在广州碳市场的经验分析中表现出卓越的表现.
- 实现了低误差指标:根平均平方误差 (RMSE) 为0.1716,平均绝对误差 (MAE) 为0.1218,平均绝对百分比误差 (MAPE) 为0.0026.
- 在预测碳价格准确性方面表现优于比较模型,这表明预测能力得到了增强.
结论:
- 开发的多因素HKELM模型在碳价格预测方面取得了重大进展.
- 二次分解,集体学习和优化的HKELM的结合为市场参与者提供了强大的工具.
- 这项研究为预测碳价格的理论和方法框架做出了贡献,特别是在复杂的区域市场.
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