一种新的石油价格预测方法,使用复杂性特征的递归分解-重建-组装方法
Fang Wang1,2, Menggang Li2,3, Ruopeng Wang4
1School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China.
Entropy (Basel, Switzerland)
|July 29, 2023
概括
本研究引入了一种用于预测原油价格的新递归模型,该模型使用了带有适应噪声的完整集体实证模式分解 (CEEMDAN). 与现有模型相比,新方法显示出更高的预测准确度.
科学领域:
- 经济学 经济学 经济学
- 金融市场 金融市场
- 时间序列分析时间序列分析
背景情况:
- 由于原油价格的广泛经济影响,预测原油价格至关重要.
- 现有的预测方法经常与石油价格数据的复杂,非线性性质作斗争.
研究的目的:
- 为提高原油价格预测提出一种新的递归分解-重建-整体模型.
- 为了解决石油价格数据固有的复杂性和动态特征.
主要方法:
- 使用完整的集体实证模式分解与自适应噪声 (CEEMDAN) 进行数据分解.
- 开发了一种复杂模型,包括模式重建,组件预测和由复杂性特征驱动的集体预测.
- 使用西德克萨斯中间价 (WTI) 和布伦特原油现货价格验证了该模型.
主要成果:
- 拟议的递归CEEMDAN分解-重建-整体模型表现出卓越的预测性能.
- 经验结果证实了该模型对基准预测技术的有效性.
- 该模型成功地捕获了原油价格数据的复杂性特征.
结论:
- 循环的CEEMDAN分解-重建-整体模型是未来石油价格预测的有效工具.
- 这种方法为预测波动的原油市场提供了更高的准确性和可靠性.
- 这些发现支持学术界和政策制定者采用这种先进方法.
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