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arXiv AI By Jinjin Chi, Lei Feng, Yiming Wang, Lulu Zhang, Yongcheng Jing, Chenyi Li, Ximing Li

ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting

Read the original on arXiv AI →

ROAR is a Retrieval Opportunity-Aware Refinement framework designed for zero-shot time series forecasting. It enhances predictions by weighting queries according to base-forecast difficulty and the improvement potential of retrieved historical candidates, then learning a gate to control correction strength. The method jointly calibrates the forecasting module and gate, anchoring the final prediction to an initial refined output, and achieves the lowest average MSE across seven benchmarks compared to other methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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