Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision
Retrieval-augmented time-series forecasting typically selects historical examples by similarity between observed pasts, although similar pasts can evolve differently. We propose Predictive Relevance Retrieval (PRR), which uses realized future compatibility as privileged supervision to learn a retrieval function that remains strictly past-only at inference. PRR combines Pearson retrieval with a futuresupervised predictive representation to expand candidate support, then reranks the union using statistical and learned pair relations. Across six datasets and four long horizons, PRR improves Pearson retrieval in 23 of 24 conditions, reducing AnalogFutureMSE by 26.7% on average. A candidate-bu...
| Authors | Yong-Hoon Choi, Kwang-Hyun Park, Youngjin Cho |
|---|---|
| Primary category | cs.IR |
| Published | 24 August 2026 |
| Last updated at source | 26 September 2026 |
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