融合静态与动态关系，提升股票价格预测精度
Stock Prediction via a Dual Relation Fusion Network incorporating Static and Dynamic Relations
- 设计双关系融合网络，同时建模长期稳定关系与短期变化
- 在多市场测试中超越基线，对股价联动敏感度高
- 适合关注金融时序建模与市场关系挖掘的研究者
准确建模股票间关系对股价预测至关重要。现有方法多聚焦单一状态关系，忽视动态与静态关系的互补性。为此，我们提出双关系融合网络（DRFN），捕捉股票关系结构的长期相对稳定性，同时保持对突发市场波动的响应能力。方法包含新型相对静态关系模块，用于建模随时间变化的长期模式并融入隔夜信息影响；通过距离感知机制捕捉动态股票关系，并利用前一日动态关系与预设静态关系的循环融合来演化长期结构。实验表明，该方法在不同市场中显著优于基线模型，对关系强度与股价协同变动具有高度敏感性。
原文摘要 · Abstract (English)
Accurate modeling of inter-stock relationships is critical for stock price forecasting. However, existing methods predominantly focus on single-state relationships, neglecting the essential complementarity between dynamic and static inter-stock relations. To solve this problem, we propose a Dual Relation Fusion Network (DRFN) to capture the long-term relative stability of stock relation structures while retaining the flexibility to respond to sudden market shifts. Our approach features a novel relative static relation component that models time-varying long-term patterns and incorporates overnight informational influences. We capture dynamic inter-stock relationships through distance-aware mechanisms, while evolving long-term structures via recurrent fusion of dynamic relations from the prior day with the pre-defined static relations. Experiments demonstrate that our method significantly outperforms the baselines across different markets, with high sensitivity to the co-movement of relational strength and stock price.
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