Understanding the Limits of Deep Tabular Methods with Temporal Shift

Understand

Deep tabular models have demonstrated remarkable success on i.i.d.

• data, excelling in a variety of structured data tasks.
• However, their performance often deteriorates under temporal distribution shifts, where trends and periodic patterns are present in the evolving data distribution over time.
• In this paper, we explore the underlying reasons for this failure in capturing temporal dependencies.