基于多重分解和XGBoost算法的碳价格预测
Ke Xu1, Zhanguo Xia2, Miao Cheng3
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China.
概括
一个新的多分解XGBOOST模型提高了碳价格预测的准确性. 这种方法提高了波动性和稳定的碳市场的预测,为决策者和市场参与者提供了更好的洞察力.
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:
- 碳交易是控制全球二氧化碳排放的关键机制.
- 碳定价极大地影响了市场参与者和政策制定者的决策.
- 准确的碳价格预测对于有效的市场管理至关重要.
研究的目的:
- 提出和评估一个新的碳价格预测模型,名为多分解-XGBOOST.
- 在不同的市场条件下提高碳价格预测的准确性.
- 为市场参与者和政策制定者提供一个强大的工具.
主要方法:
- 使用完整的集体实证模式分解与自适应噪声 (CEEMDAN) 进行初始价格序列分解.
- 应用变量模式分解 (VMD) 到具有最高样本的内在模式函数 (IMF).
- 基于样本的重组IMF,随后进行进一步的CEEMDAN分解和XGBoost预测.
主要成果:
- 多分解XGBOOST模型在北京和湖北碳市场都表现出卓越的预测性能.
- 在北京市场,与单一的XGBoost.相比,该模型分别提高了RMSE,MAE和MAPE30.4%,44.5%和42.9%,相比之下,单一的XGBoost.
- 在湖北市场,RMSE,MAE和MAPE分别下降了28.5%,39.4%和39.4%.
结论:
- 拟议的多分解-XGBOOST模型为碳价格提供了显著改善的预测准确性.
- 该模型的有效性涵盖了高度波动和相对稳定的碳市场.
- 这种方法为了解和预测碳市场动态提供了可靠的工具.
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