用改进GAN模型预测股票，更准更稳。
Encoder Decoder Generative Adversarial Network Model for Stock Market Prediction
- 用残差GRU构建编码器-解码器生成器，提升时序建模能力
- 引入动态与静态协变量条件，增强上下文学习效果
- 在波动市场中仍保持高精度和训练稳定，适合金融预测场景
由于金融市场具有高度波动性和非线性特征，股票价格预测依然充满挑战。尽管深度学习前景广阔，但生成对抗网络（GAN）在该领域应用受限，主要源于模式崩溃、训练不稳定以及难以捕捉时间与特征层面的关联。本文提出一种基于GRU的编码器-解码器生成对抗网络（EDGAN），在表达能力和模型简洁性之间取得平衡。该模型引入关键创新：带有残差连接的时间解码器以实现精准重建，对静态与动态协变量进行条件化以支持上下文学习，并采用窗口机制捕捉时间动态。生成器采用密集编码器-解码器结构，结合残差GRU模块。在多个股票数据集上的大量实验表明，EDGAN在复杂市场条件下实现了更优的预测精度与训练稳定性，其性能持续优于传统GAN变体，在预测准确率与收敛稳定性方面均表现更佳。
原文摘要 · Abstract (English)
Forecasting stock prices remains challenging due to the volatile and non-linear nature of financial markets. Despite the promise of deep learning, issues such as mode collapse, unstable training, and difficulty in capturing temporal and feature level correlations have limited the applications of GANs in this domain. We propose a GRU-based Encoder-Decoder GAN (EDGAN) model that strikes a balance between expressive power and simplicity. The model introduces key innovations such as a temporal decoder with residual connections for precise reconstruction, conditioning on static and dynamic covariates for contextual learning, and a windowing mechanism to capture temporal dynamics. Here, the generator uses a dense encoder-decoder framework with residual GRU blocks. Extensive experiments on diverse stock datasets demonstrate that EDGAN achieves superior forecasting accuracy and training stability, even in volatile markets. It consistently outperforms traditional GAN variants in forecasting accuracy and convergence stability under market conditions.
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