原油价格预测基于混合频深度学习方法和智能优化算法
1School of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China.
Entropy (Basel, Switzerland)
|May 24, 2024
概括
预测原油价格是复杂的. 这项研究引入了一种混合模型,将深度学习和计量经济学的方法结合起来,提高了对波动性石油市场的预测准确度.
科学领域:
- 经济学 经济学 经济学
- 金融预测 金融预测
- 数据科学数据科学数据科学
背景情况:
- 由于非线性,波动性和随机性,原油价格预测本质上很困难.
- 现有的模型往往难以有效地捕捉石油价格的复杂动态.
研究的目的:
- 引入一种新的混合模型,KV-MFSCBA-G,用于增强原油价格预测.
- 为了提高准确性,利用深度学习和传统计量经济学模型的优势.
主要方法:
- 使用Kullback-Leibler分歧优化的变化模式分解 (KL-VMD) 的分解-整合范式.
- 混合模型结合了混合频率卷积神经网络双向长期短期记忆网络注意力机制 (MFCBA) 和通用自回归条件异种性 (GARCH).
- 搜索算法 (SSA) 用于参数优化和纳入全球经济状况 (GECON) 数据.
主要成果:
- 拟议的KV-MFSCBA-G模型在其他基准模型中表现出优越的性能.
- 西德克萨斯中间油 (WTI) 和布伦特原油的实证结果显示,评估指标和统计测试的显著改善.
- 该模型在预测能力方面表现出良好的稳定性.
结论:
- 混合KV-MFSCBA-G模型有效地解决了原油价格预测的挑战.
- 这种方法为投资者和市场监管机构提供了一个有价值的工具,帮助他们做出明智的决策.
- 深度学习,计量经济学和智能优化的整合提高了波动性市场的预测准确性.
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