AI RESEARCH
Towards Principled Test-Time Adaptation for Time Series Forecasting
arXiv CS.LG
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ArXi:2605.17250v1 Announce Type: new Test-time adaptation (TTA) has recently emerged as a promising approach for improving time series forecasting (TSF) under distribution shift. Existing TSF-TTA methods differ in how they utilize revealed targets, yet the resulting adaptation protocols remain heterogeneous and lack a clearly unified formulation. To address this issue, we revisit TSF-TTA from the perspective of protocol cleanliness and propose an adaptation protocol based solely on matured ground truth, yielding a principled setting for adaptation.