Towards Principled Test-Time Adaptation for Time Series Forecasting

2026-05-17

AI Summary

This work addresses the lack of a standardized protocol in test-time adaptation for time series forecasting and the limited robustness of existing methods under distribution shifts. The study reformulates test-time adaptation from the perspective of protocol rigor, introducing a clean adaptation protocol that relies solely on observed ground-truth values. Through frequency-domain analysis, it reveals fundamental limitations in current approaches. Building upon these insights, the authors design a lightweight frequency-domain parameterized calibration module that consistently achieves strong performance across diverse datasets, forecast horizons, and backbone models. The proposed method significantly reduces parameter count compared to existing techniques while maintaining architectural clarity and computational efficiency.

Abstract

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 more principled setting for adaptation. Under this protocol, we further diagnose existing adapters in the frequency domain and find that their prediction corrections often exhibit limited and weakly structured spectral modifications. Motivated by this diagnosis, we propose Frequency-Aware Calibration (FAC), a lightweight calibration method that directly parameterizes prediction corrections in the frequency domain. Across diverse datasets, forecasting horizons, and source forecasters, FAC achieves competitive and consistent performance while requiring substantially fewer trainable parameters than the compared TSF-TTA adapters.

Problem

• test-time adaptation
• time series forecasting
• distribution shift
• adaptation protocol
• frequency domain

Innovation

• test-time adaptation
• time series forecasting
• frequency domain
• principled protocol
• lightweight calibration