aiqbench analysis
ROAR introduces a novel framework for zero-shot time series forecasting by dynamically weighting retrieved historical candidates based on base-forecast difficulty and potential improvement. The method employs a learned gating mechanism to calibrate correction strength, achieving state-of-the-art performance across seven benchmarks with multiple backbone architectures. This work advances the field of machine learning by addressing the limitations of standard retrieval augmentation in sequential prediction tasks.
ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting
arXiv:2605.24911v2 Announce Type: replace-cross Abstract: Retrieval augmentation provides time series forecasters with historical continuations, yet even candidates that outperform the base forecast may fail to improve the final prediction. We propose ROAR, a Retrieval Opportunity-Aware Refinement framework for zero-shot time series forecasting. To better exploit these improvement opportunities, its training objective allocates additional emphasis across queries based on base-forecast difficulty and the relative improvement offered by retrieved candidates. Using this objective, ROAR first learns to aggregate aligned historical candidates and uses a learned gate to control their correction strength against a fixed base forecaster. It then jointly calibrates the forecasting module and gate to coordinate their contributions, while anchoring the combined prediction to the first-stage refined output. We derive exact decompositions of refinement gains and the opportunity-weighted training loss, and characterize additional gains from joint calibration under a local linearization. Experiments on seven benchmarks show that ROAR achieves the lowest average MSE among the compared methods. Further evaluations demonstrate average forecasting improvements across multiple backbone families and retrieval-augmented forecasters.
Abstract:Retrieval augmentation provides time series forecasters with historical continuations, yet even candidates that outperform the base forecast may fail to improve the final prediction. We propose ROAR, a Retrieval Opportunity-Aware Refinement framework for zero-shot time series forecasting. To better exploit these improvement opportunities, its training objective allocates additional emphasis across queries based on base-forecast difficulty and the relative improvement offered by retrieved candidates. Using this objective, ROAR first learns to aggregate aligned historical candidates and uses a learned gate to control their correction strength against a fixed base forecaster. It then jointly calibrates the forecasting module and gate to coordinate their contributions, while anchoring the combined prediction to the first-stage refined output. We derive exact decompositions of refinement gains and the opportunity-weighted training loss, and characterize additional gains from joint calibration under a local linearization. Experiments on seven benchmarks show that ROAR achieves the lowest average MSE among the compared methods. Further evaluations demonstrate average forecasting improvements across multiple backbone families and retrieval-augmented forecasters.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2605.24911 [cs.LG]
(or arXiv:2605.24911v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.24911 Focus to learn more arXiv-issued DOI via DataCite
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