MATE: Delayed Credit Assignment for Test-Time Adaptation in Time-Series Forecasting

Distribution shifts can undermine time-series forecasting accuracy after deployment. Test-time adaptation for time-series forecasting (TSF-TTA) can mitigate these errors, but a single correction mechanism can struggle with complex and abrupt shifts. Its effectiveness also varies across forecast leads and variable channels. Delayed target availability further complicates learning where and how strongly to apply each correction. We introduce MATE (Matured-Feedback Adaptive Test-Time Error Correction) to address these challenges. MATE separates correction generation from credit allocation under a full-window feedback protocol. It keeps the pretrained backbone frozen and constructs complementary corrections through a structured residual dictionary. As feedback matures, MATE accumulates credit statistics to guide lead–channel credit weights and regulate each correction’s contribution. It also updates input and output calibrators using matured historical forecast errors to capture distribution shifts. Comparisons across datasets, forecasting backbones, and shift scenarios demonstrate broad forecasting gains. Mechanism ablations further support the effectiveness of position-specific correction control driven by matured feedback.