Feature-aware Modulation for Learning from Temporal Tabular Data

2025-12-03

AI Summary

In real-world temporal tabular data, the mapping between features and labels continually evolves, inducing temporal distribution shifts that undermine static models’ generalizability and cause adaptive models to overfit transient patterns. To address this, we propose Feature-Aware Temporal Modulation (FATM), the first method to jointly model the co-evolution of features’ objective semantics (statistical distributions) and subjective semantics (task-specific relevance). FATM employs a lightweight time-contextual modulation mechanism to dynamically adjust features’ statistical properties—such as scale and skewness—enabling cross-temporal feature alignment and adaptive mapping. Evaluated on multiple benchmark datasets under distribution shift, FATM significantly improves model robustness and prediction accuracy. It effectively balances generalization and adaptation without architectural complexity or excessive parameter overhead.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs

Abstract

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.

Problem

• Addresses temporal distribution shifts in tabular data due to evolving feature semantics.
• Mitigates concept drift by aligning feature representations across different time stages.
• Balances model robustness and adaptability through feature-aware temporal modulation.

Innovation

• Feature-aware modulation conditions representations on temporal context
• Aligns feature semantics across time to balance adaptability
• Modulates statistical properties like scale and skewness dynamically