Tang Yongli, Gao Zhenlun, Cai Zhongqi, Yu Jinxia, Qin Panke
School of Software, Henan Polytechnic University, Jiaozuo, Henan, China.
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, China.
PeerJ Comput Sci. 2025 Jun 2;11:e2865. doi: 10.7717/peerj-cs.2865. eCollection 2025.
Financial market prediction faces significant challenges due to the complex temporal dependencies and heterogeneous data relationships inherent in futures price-spread data. Traditional machine learning methods struggle to effectively mine these patterns, while conventional long short-term memory (LSTM) models lack focused feature prioritization and suffer from suboptimal hyperparameter selection. This article proposes the Improved Grey Wolf Optimizer with Multi-headed Self-attention and LSTM (IGML) model, which integrates a multi-head self-attention mechanism to enhance feature interaction and introduces an improved grey wolf optimizer (IGWO) with four strategic enhancements for automated hyperparameter tuning. Benchmark tests on optimization problems validate IGWO's superior convergence efficiency. Evaluated on real futures price-spread datasets, the IGML reduces mean square error (RMSE) and mean absolute error (MAE) by up to 88% and 85%, respectively, compared to baseline models, demonstrating its practical efficacy in capturing intricate financial market dynamics.
由于期货价格价差数据中固有的复杂时间依赖性和异构数据关系，金融市场预测面临重大挑战。传统机器学习方法难以有效挖掘这些模式，而传统的长短期记忆（LSTM）模型缺乏重点特征优先级，且超参数选择欠佳。本文提出了具有多头自注意力和LSTM的改进灰狼优化器（IGML）模型，该模型集成了多头自注意力机制以增强特征交互，并引入了具有四项策略增强功能的改进灰狼优化器（IGWO）用于自动超参数调整。对优化问题的基准测试验证了IGWO卓越的收敛效率。在真实期货价格价差数据集上进行评估时，与基线模型相比，IGML的均方根误差（RMSE）和平均绝对误差（MAE）分别降低了高达88%和85%，证明了其在捕捉复杂金融市场动态方面的实际功效。
