动态调整特征表示以应对时间数据分布漂移，提升模型适应性与稳定性。
Feature-aware Modulation for Learning from Temporal Tabular Data
- 基于时间上下文动态调节特征统计特性，如尺度与偏度。
- 在多个基准数据集上显著降低时间漂移导致的性能下降。
- 适合处理随时间变化的时序表格数据，如金融风控、用户行为预测。
尽管表格机器学习已取得显著进展，但现实部署中持续演变的特征与标签关系带来挑战。静态模型假设映射关系固定以保障泛化能力，而自适应模型可能过拟合于短暂模式，陷入鲁棒性与适应性之间的两难。本文分析构建有效时序表格数据动态映射的关键因素，发现特征语义（尤其是客观与主观含义）的演化引入概念漂移。关键发现是，特征变换策略可缓解不同时期特征表示间的差异。受此启发，我们提出一种特征感知的时间调制机制，通过时间上下文条件化特征表示，动态调节其尺度、偏度等统计属性。该方法通过对齐不同时期的特征语义，实现轻量级但强大的自适应能力，在保持泛化性的同时增强灵活性。基准测试验证了该方法在应对表格数据时间分布漂移方面的有效性。
原文摘要 · Abstract (English)
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.
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