AI Insight
ROAR is a new framework for zero-shot time series forecasting that improves predictions by intelligently incorporating relevant historical data patterns. The system uses a two-stage approach: first learning to aggregate similar historical patterns from a database, then calibrating how strongly these patterns should adjust the base forecast based on predicted improvement opportunities. Testing across seven benchmark datasets showed ROAR achieved the lowest average mean squared error compared to existing methods.
Why it matters
This advance could improve forecasting accuracy in domains where historical patterns are informative but training data is limited, such as predicting demand for new products, forecasting in emerging markets, or anticipating trends in rapidly changing environments where traditional models struggle.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
-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.
Source: ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting