Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift

ArXi:2510.14814v2 Announce Type: replace Time-series forecasting finds broad applications in real-world scenarios. Due to the dynamic nature of time series data, it is important for time-series forecasting models to handle potential distribution shifts over time. In this paper, we initially identify two types of distribution shifts in time series: concept drift and temporal shift.