Enhanced futures price-spread forecasting based on an attention-driven optimized LSTM network: integrating an improved grey wolf optimizer algorithm for enhanced accuracy.

Abstract

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.