TRACE: Retrieval-Augmented Error Correction for Time-Series Forecasting

Retrieval-augmented time-series forecasting has emerged as a promising way to complement learned forecasters with historical analogues. However, directly transferring a retrieved future can overwrite level, phase, and dynamics that the backbone forecasting model already predicts correctly. This tension raises a natural question: can retrieval preserve what the forecaster already gets right while correcting what it tends to miss? To investigate this question, we introduce TRACE (Time-series Retrieval-Augmented Correction from Errors), a post-hoc framework that retrieves model-specific historical forecast errors. Rather than treating retrieved futures as replacement predictions, TRACE anchors its correction on the base forecast and retrieves errors from states characterized by both the observed context and the forecaster's prediction. It further shrinks unreliable corrections and adaptively controls how much additional trajectory information to borrow from historical analogues, without modifying the forecasting backbone. Across 585 controlled settings spanning 16 datasets and 13 forecasting backbones, TRACE strictly improves all reported metrics in 92.5% of cases, demonstrating consistent gains across diverse forecasting conditions. Complementary end-to-end evaluation further confirms that error-based correction generalizes across independently trained forecasters. More importantly, our analysis reveals that error retrieval becomes increasingly favorable as the underlying forecaster grows stronger, while the benefit of borrowing retrieved trajectories diminishes with forecast horizon. These results suggest that as forecasting models improve, retrieval is increasingly valuable for identifying how the model is likely to be wrong, rather than for supplying another complete future.