COSA: Context-aware Output-Space Adapter for Test-Time Adaptation in Time Series Forecasting

ICLR 2026 Conference Submission6831 Authors

Keywords: Test-Time Adaptation, Time-Series Forecasting, Simple Adapter

Abstract:

Deployed time-series forecasters suffer performance degradation under non-stationarity and distribution shifts.
Test-time adaptation (TTA) for time-series forecasting differs from vision TTA because ground truth becomes observable shortly after prediction.
Existing time-series TTA methods typically employ dual input/output adapters that indirectly modify data distributions, making their effect on the frozen model difficult to analyze.
We introduce the Context-aware Output-Space Adapter (COSA), a minimal, plug-and-play adapter that directly corrects predictions of a frozen base model.
COSA performs residual correction modulated by gating, utilizing the original prediction and a lightweight context vector that summarizes statistics from recently observed ground truth.
At test time, only the adapter parameters (linear layer and gating) are updated under a leakage-free protocol, using observed ground truth with an adaptive learning rate schedule for faster adaptation.
Across diverse scenarios, COSA demonstrates substantial performance gains versus baselines without TTA (13.91$\sim$17.03\%) and SOTA TTA methods (10.48$\sim$13.05\%), with particularly large improvements at long horizons, while adding a reasonable level of parameters and negligible computational overhead.
The simplicity of COSA makes it architecture-agnostic and deployment-friendly.
Source code: https://anonymous.4open.science/r/linear-adapter-A720

Primary Area: learning on time series and dynamical systems

Submission Number: 6831