FATE-Net: An Optimization-Enhanced Attention-Driven Temporal Evolution Framework for Stock Price Forecasting

Stock price forecasting remains challenging due to the nonlinear, volatile, multi-scale dynamics of financial time series. This study addresses two core limitations of existing models: incomplete capture of full-spectrum multi-scale temporal dependencies and severe hyperparameter sensitivity caused by inefficient manual tuning. To solve these issues, we propose FATE-Net, an optimization-enhanced attention-driven forecasting framework. FATE-Net first integrates LSTM-based local encoding and Transformer-based global refinement to model multi-scale temporal dependencies. To address hyperparameter sensitivity, we embed a multi-objective particle swarm optimization (MOPSO) strategy, which formulates hyperparameter configuration as a dual-objective problem minimizing MAPE and RMSE, automatically exploring the hyperparameter space to find optimal configurations and enhance model generalization. Experiments on BYD stock data show that FATE-Net achieves state-of-the-art performance, with an MAE of 1.051, RMSE of 1.435, MAPE of 0.37%, and R2 of 0.997, verifying our framework’s effectiveness.