用新数据增强方法提升Transformer预测股市指数的准确性与稳定性
Robust Transformer-Based One-Step Stock Index Forecasting via Shifted Data Augmentation
- 引入移位数据增强技术，缓解金融时序噪声与分布偏移问题
- 在VN30和标普500上误差显著降低，且对超参数不敏感
- 适合追求高鲁棒性的量化交易或金融预测场景
Transformer在序列建模中表现优异，但直接应用于金融时间序列仍面临信号嘈杂、记忆短和分布漂移等挑战。本文提出一种改进的Transformer架构，结合先进的学习率调度策略和一种新型移位数据增强（SDA）技术。在越南VN30和标普500两个基准股市指数数据集上进行评估，结果表明：带预热的余弦退火调度相比广义反幂调度能持续提升预测精度；同时，SDA显著降低预测误差和运行间波动，并增强对超参数选择的鲁棒性。两者结合在两个数据集上均取得最佳性能，表明在基于Transformer的金融预测中，数据增强的作用可能超过增加模型复杂度。该方法为噪声环境下提供了一种高效且稳健的股市指数预测方案。
原文摘要 · Abstract (English)
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.
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