MaGNet：基于时间-因果与全局关系学习的 Mamba 双超图网络用于股票预测
MaGNet: A Mamba Dual-Hypergraph Network for Stock Prediction via Temporal-Causal and Global Relational Learning
AI总结：
本文提出 MaGNet，一种结合 Mamba 与双超图框架的股票预测网络，通过时间因果超图和全局概率超图分别捕捉局部时间影响与瞬时全局结构，在六大股票指数上实现了优于现有方法的预测与投资回报。
摘要：
股票趋势预测对盈利交易策略和投资组合管理至关重要，但由于市场波动性、复杂的时间动态以及多方面的股票间关系而仍具挑战性。现有方法难以有效捕捉时间依赖性和动态的股票间交互，通常忽略横截面市场影响，依赖静态相关性，对节点和边采用统一处理方式，并混淆了多种关系。本研究提出了 MaGNet，一种用于股票预测的新型 Mamba 双超图网络，融合了三项关键创新：（1）MAGE 模块，利用带有自适应门控机制的双向 Mamba 进行上下文时间建模，并集成稀疏 Mixture-of-Experts 层以实现对多样市场条件的动态适应，同时采用多头注意力捕捉全局依赖；（2）特征级和股票级 2D 时空注意力模块，实现多变量特征与跨股票依赖的精确融合，在保留内在数据结构的同时有效增强信息量，将时间建模与关系推理相桥接；（3）双超图框架，由捕捉具有时间约束的细粒度因果依赖的 Temporal-Causal Hypergraph (TCH) 和通过软超边分配及 Jensen-Shannon Divergence 加权机制建模全市场模式的 Global Probabilistic Hypergraph (GPH) 组成，共同将局部时间影响与瞬时全局结构解耦，以实现多尺度关系学习。在六个主要股票指数上的大量实验表明，MaGNet 在卓越的预测性能和出色的投资回报方面均优于现有最先进方法，并具备稳健的风险管理能力。代码获取：https://github.com/PeilinTime/MaGNet。
英文摘要：
Stock trend prediction is crucial for profitable trading strategies and portfolio management yet remains challenging due to market volatility, complex temporal dynamics and multifaceted inter-stock relationships. Existing methods struggle to effectively capture temporal dependencies and dynamic inter-stock interactions, often neglecting cross-sectional market influences, relying on static correlations, employing uniform treatments of nodes and edges, and conflating diverse relationships. This work introduces MaGNet, a novel Mamba dual-hyperGraph Network for stock prediction, integrating three key innovations: (1) a MAGE block, which leverages bidirectional Mamba with adaptive gating mechanisms for contextual temporal modeling and integrates a sparse Mixture-of-Experts layer to enable dynamic adaptation to diverse market conditions, alongside multi-head attention for capturing global dependencies; (2) Feature-wise and Stock-wise 2D Spatiotemporal Attention modules enable precise fusion of multivariate features and cross-stock dependencies, effectively enhancing informativeness while preserving intrinsic data structures, bridging temporal modeling with relational reasoning; and (3) a dual hypergraph framework consisting of the Temporal-Causal Hypergraph (TCH) that captures fine-grained causal dependencies with temporal constraints, and Global Probabilistic Hypergraph (GPH) that models market-wide patterns through soft hyperedge assignments and Jensen-Shannon Divergence weighting mechanism, jointly disentangling localized temporal influences from instantaneous global structures for multi-scale relational learning. Extensive experiments on six major stock indices demonstrate MaGNet outperforms state-of-the-art methods in both superior predictive performance and exceptional investment returns with robust risk management capabilities. Codes available at: https://github.com/PeilinTime/MaGNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
