arXiv Machine Learning
Robust Residual Correction via Selective Deployment for Time Series Forecasting
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arXiv AI
Oct 19
Look Back, Correct Forward: Error Feedback Calibration for Time Series Forecasting
The paper introduces Error Feedback Calibration (EFC), a test‑time framework that corrects deterministic time‑series forecasts by using revealed errors as causal feedback. EFC builds a Causal Error Diagonal (CED) from overlapping historical forecast errors, applies Cumulative Error Transport (CET) to map this error state to future residuals, and employs Temporal Residual Learning (TRL) to predict remaining residual structure. Experiments across various forecasting backbones and datasets demonstrate that EFC improves performance while limiting over‑correction.
arXiv Machine Learning
Jun 4
Post-Training Corrections for Improved Time-Series Forecasting
arXiv:2505. 15354v3 Announce Type: replace Abstract: Time-series forecasting is a critical task in various business domains, but it remains inherently challenging.
arXiv AI
Jun 29
Deployment-Side Adaptiveness in Multi-Horizon Volatility Forecasting
arXiv:2606. 27688v1 Announce Type: cross Abstract: In financial forecasting, predictive performance depends not only on which model is trained, but also on how the trained model is deployed.
arXiv Machine Learning
Oct 7
The Premise Is the Problem: Exchangeability Failure in Self-Monitored Test-Time Adaptation
arXiv:2610.07038v1 Announce Type: new Abstract: Modern forecasting models are often updated after deployment so they can respond to changing data. These updates can also make predictions worse, so pr...
arXiv AI
Oct 1
Pseudo-Label-Triggered Retraining from Forecast Errors for Online Time Series Forecasting
arXiv:2609.39789v1 Announce Type: cross Abstract: Real-world time series forecasting systems operate under non-stationary data streams, where forecasting performance may degrade over time. Although r...
arXiv AI
Sep 4
RATL: Learning from Retrieved Residuals for Robust Multivariate Time-Series Forecasting
RATL is a plug‑in method for multivariate time‑series forecasting that uses a frozen base forecaster to build a memory of its historical forecast residuals. During inference, RATL retrieves residual trajectories from similar past contexts and employs a set‑aware router to combine them, providing learned feedback correction. Experiments demonstrate that this residual‑retrieval approach improves the performance of the base forecaster across various benchmarks and backbones.