RV-FELM:基于RIME-VMD算法以及FA-ELM的期货商品价格预测
Xiong Yang1, Zihang Zhang2, Huihua Xu3
1Fuzhou University Zhicheng College, Fuzhou, China.
Heliyon
|September 16, 2024
概括
本研究引入了一种新的机器学习组合方法,用于准确预测商品期货价格. 该模型结合了变化模式分解 (VMD) 和极端学习机器 (ELM) 来提高原油和大豆的预测准确性.
科学领域:
- 金融工程是金融工程.
- 计算经济学 计算经济学
- 机器学习 机器学习
背景情况:
- 商品期货对于物质贸易对冲至关重要.
- 准确的价格预测使国家和企业能够做出明智的生产和消费决策.
研究的目的:
- 为商品期货价格预测引入一种新的机器学习合并方法.
- 通过结合分解和物理优化算法来提高预测准确度.
主要方法:
- 由Rime优化算法 (RIME) 优化的变化模式分解 (VMD).
- 使用极端学习机器 (ELM) 和富里埃注意力 (FA) 模型预测趋势和季节性术语.
- 对最终商品期货价格预测的预测结果的综合.
主要成果:
- 对于原油价格来说,实现的低平均绝对百分比误差 (MAPE):0.48% (1 步),0.66% (3 步),0.75% (6 步).
- 在大豆价格方面实现了低MAPE:0.22% (1步),0.27% (3步),0.37% (6步).
- 在水平和定向准确性方面表现优于基准模型,证明了稳定性.
结论:
- 拟议的整体模型有效地捕捉了商品期货系列的时间和频率域特征.
- 该方法在预测原油和大豆期货价格方面提供了卓越的准确性和稳定性.
- 这种方法为商品市场的对冲和决策提供了有价值的工具.
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