Transformers have shown remarkable success in sequence modeling, yet their direct application to financial time series remains challenging due to noisy signals, short-memory dynamics, and distributional shifts. This paper proposes a modified Transformer architecture for one-step stock index forecasting, combined with advanced learning-rate scheduling and a novel Shifted Data Augmentation (SDA) technique. We evaluate the proposed framework on two benchmark stock index datasets, VN30 and S&P 500. Experimental results demonstrate that cosine annealing with warmup consistently improves forecasting accuracy over the generalized inverse-power scheduler. Furthermore, SDA substantially reduces forecasting errors and run-to-run variability while improving robustness to hyperparameter selection. The combination of cosine annealing scheduling and SDA achieved the best performance on both datasets, indicating that data augmentation can play a more important role than increasing model complexity in Transformer-based financial forecasting. These findings provide a practical and computationally efficient approach for robust stock index forecasting in noisy financial environments.

翻译：Transformer在序列建模中展现了显著的成功，然而由于噪声信号、短记忆动态和分布偏移，其在金融时间序列上的直接应用仍面临挑战。本文提出了一种改进的Transformer架构，用于一步股票指数预测，结合了先进的学习率调度策略和一种新颖的移位数据增强技术。我们在两个基准股票指数数据集VN30和S&P 500上评估了所提出的框架。实验结果表明，与广义逆幂调度器相比，带有预热的余弦退火能持续提高预测精度。此外，移位数据增强大幅降低了预测误差和运行间变异性，同时提高了对超参数选择的鲁棒性。余弦退火调度与移位数据增强的组合在两个数据集上均取得了最佳性能，这表明在基于Transformer的金融预测中，数据增强可以比增加模型复杂性发挥更重要的作用。这些发现为在嘈杂的金融环境中进行鲁棒的股票指数预测提供了一种实用且计算高效的方法。