[2510.14814] Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift

Computer Science > Machine Learning arXiv:2510.14814 (cs) [Submitted on 16 Oct 2025 (v1), last revised 25 Mar 2026 (this version, v2)] Title:Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift Authors:Zhiyuan Zhao, Haoxin Liu, B. Aditya Prakash View a PDF of the paper titled Tackling Time-Series Forecasting Generalization via Mitigating Concept Drift, by Zhiyuan Zhao and 2 other authors View PDF HTML (experimental) Abstract: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. We acknowledge that while existing studies primarily focus on addressing temporal shift issues in time series forecasting, designing proper concept drift methods for time series forecasting has received comparatively less attention. Motivated by the need to address potential concept drift, while conventional concept drift methods via invariant learning face certain challenges in time-series forecasting, we propose a soft attention mechanism that finds invariant patterns from both lookback and horizon time series. Additionally, we emphasize the critical importance of mitigating temporal shifts as a preliminary to addressing concept drift. In this context, we introduce ShifTS, a method-agnostic...