用Mamba+图神经网络预测股价，速度快精度高。
Mamba Meets Financial Markets: A Graph-Mamba Approach for Stock Price Prediction
- 用双向Mamba捕捉长期价格依赖，结合自适应图卷积建模股票特征关联。
- 在多个数据集上超越现有模型，预测误差降低12%-18%。
- 适合需要实时、长序列金融预测的量化交易场景。
股票市场在全球经济中扮演关键角色，准确的股价预测可带来显著财务回报。尽管现有基于Transformer的模型在金融时间序列预测中已优于LSTM和CNN，但其高计算复杂度和内存需求限制了其实时交易与长序列处理的实用性。为此，我们提出SAMBA框架，基于Mamba架构并融合图神经网络，通过双向Mamba模块捕捉历史价格的长期依赖，并利用自适应图卷积建模每日股票特征间的关联，实现近线性计算复杂度。实验表明，SAMBA在多个基准数据集上显著优于现有先进模型，同时保持低计算开销。代码与数据集见github.com/Ali-Meh619/SAMBA。
原文摘要 · Abstract (English)
Stock markets play an important role in the global economy, where accurate stock price predictions can lead to significant financial returns. While existing transformer-based models have outperformed long short-term memory networks and convolutional neural networks in financial time series prediction, their high computational complexity and memory requirements limit their practicality for real-time trading and long-sequence data processing. To address these challenges, we propose SAMBA, an innovative framework for stock return prediction that builds on the Mamba architecture and integrates graph neural networks. SAMBA achieves near-linear computational complexity by utilizing a bidirectional Mamba block to capture long-term dependencies in historical price data and employing adaptive graph convolution to model dependencies between daily stock features. Our experimental results demonstrate that SAMBA significantly outperforms state-of-the-art baseline models in prediction accuracy, maintaining low computational complexity. The code and datasets are available at github.com/Ali-Meh619/SAMBA.
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