While tabular machine learning has achieved remarkable success, temporal distribution shifts pose significant challenges in real-world deployment, as the relationships between features and labels continuously evolve. Static models assume fixed mappings to ensure generalization, whereas adaptive models may overfit to transient patterns, creating a dilemma between robustness and adaptability. In this paper, we analyze key factors essential for constructing an effective dynamic mapping for temporal tabular data. We discover that evolving feature semantics-particularly objective and subjective meanings-introduce concept drift over time. Crucially, we identify that feature transformation strategies are able to mitigate discrepancies in feature representations across temporal stages. Motivated by these insights, we propose a feature-aware temporal modulation mechanism that conditions feature representations on temporal context, modulating statistical properties such as scale and skewness. By aligning feature semantics across time, our approach achieves a lightweight yet powerful adaptation, effectively balancing generalizability and adaptability. Benchmark evaluations validate the effectiveness of our method in handling temporal shifts in tabular data.

翻译：尽管表格机器学习已取得显著成功，但现实场景中的时序分布偏移带来了严峻挑战，因为特征与标签之间的关联关系持续演变。静态模型依赖固定映射以确保泛化能力，而自适应模型可能对瞬时模式过拟合，从而在鲁棒性与适应性之间形成两难困境。本文系统分析了构建有效时序表格数据动态映射的关键要素。研究发现，特征语义的演化——特别是客观与主观含义的变迁——会随时间推移引发概念漂移。关键的是，我们识别出特征变换策略能够缓解不同时序阶段特征表征间的差异。基于这些发现，我们提出一种特征感知的时序调制机制，该机制将特征表征与时序上下文条件化，动态调整尺度与偏度等统计特性。通过跨时间对齐特征语义，我们的方法实现了轻量级且高效的自适应，有效平衡了泛化性与适应性。基准测试验证了该方法在处理表格数据时序偏移方面的有效性。