基于关注驱动的优化LSTM网络的增强期货价格差预测:集成了改进的灰狼优化算法,以提高准确性
Yongli Tang1, Zhenlun Gao1, Zhongqi Cai1
1School of Software, Henan Polytechnic University, Jiaozuo, Henan, China.
PeerJ. Computer science
|June 26, 2025
概括
本研究介绍了一种改进的灰狼优化器,具有多头自我注意力和LSTM (IGML) 模型,用于增强金融市场预测. 通过优化特征相互作用和超参数,IGML模型显著提高了预测期货价格差距的准确性.
科学领域:
- 计算金融是指计算金融.
- 机器学习 机器学习
- 金融计量经济学 金融计量经济学
背景情况:
- 由于复杂的时间依赖性和期货价格差的异化数据,金融市场的预测具有挑战性.
- 传统的机器学习和标准的长短期记忆 (LSTM) 模型在模式挖掘和超参数优化方面表现出局限性.
研究的目的:
- 提出一个改进的灰狼优化器,具有多头自我注意力和LSTM (IGML) 模型,用于增强期货价格传播预测.
- 改进功能交互和自动化超参数调整,用于财务时间序列预测.
主要方法:
- 整合一个多头自我注意力机制,以改善LSTM框架内的功能交互.
- 开发了一种改进的灰狼优化器 (IGWO),为自动化超参数选择提供了四个增强功能.
- 验证IGWO在基准优化问题上的收效率.
主要成果:
- 在优化任务中,IGWO算法表现出卓越的融合效率.
- IGML模型显著减少了对实际期货价格差数据集的预测错误.
- 与基线模型相比,实现了高达88%的平均平方误差 (RMSE) 和高达85%的平均绝对误差 (MAE) 的降低.
结论:
- 拟议的IGML模型有效地捕捉了复杂的金融市场动态.
- 对于期货价格扩散预测的传统方法,IGML提供了显著的进步.
- 增强的优化和注意力机制有助于卓越的预测性能.
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