突破碳价格预测的局限性:一种基于二次分解和非线性集成的新型混合模型
Yuqiao Lan1, Yubin Huangfu2, Zhehao Huang3
1School of Statistics, Southwestern University of Finance and Economics, Chengdu, China.
Journal of environmental management
|June 1, 2024
概括
准确的碳价格预测对于市场稳定和绿色发展至关重要. 结合IVMD-ICEEMDAN,SOA-LSSVM/TCN和SOA-GRU的新混合模型在碳市场上提供了更高的预测准确度.
科学领域:
- 环境经济学环境经济学
- 金融预测 财务预测
- 计算智能是一种计算智能.
背景情况:
- 碳交易对于减少排放至关重要,但价格波动带来风险.
- 需要准确的碳价格预测来稳定市场并鼓励绿色投资.
研究的目的:
- 开发一个强大的混合模型,用于精确的碳价格预测.
- 通过改进预测,加强碳市场的稳定性和支持可持续发展.
主要方法:
- 使用改进的变化模式分解 (IVMD) 和改进的完整合奏实证模式分解与适应噪声 (ICEEMDAN) 的新型二次分解.
- 用于频率识别的分散度,加上优化最小平方支向量机 (SOA-LSSVM) 的混合模型,用于高频和优化时空卷积网络 (TCN) 的低频序列.
- 集成预测使用优化的门式循环单元 (SOA-GRU) 优化通过Sine-Ott-Antoni (SOA).
主要成果:
- 拟议的IVMD-ICEEMDAN-SOALSSVM/TCN-SOAGRU模型与其他21种模型相比,实现了卓越的性能.
- 在CEEX广州市场实现了0.42%的平均绝对百分比错误 (MAPEs),在EU ETS市场达到0.83%.
- 在碳价格序列分解和预测方面表现出更好的效率和适应性.
结论:
- 新的二次分解方法解决了传统VMD的局限性,提高了分解效率.
- 混合LSSVM和TCN模型有效地捕捉了各种序列特征.
- SOA优化提高了模型的稳定性和适应性,为利益相关者提供了有价值的工具.
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