mlsanityData to 14 Oct 2026

Resolution-Aware Retrieval Augmented Zero-Shot Forecasting

arXiv 2510.16695 ↗First posted 19 Oct 2025cs.LG cs.IRcited by 0

On the map: Retrieval-augmented generation → Time series and agricultural RAG

Abstract (arXiv)

Zero-shot forecasting aims to predict outcomes for previously unseen conditions without direct historical data, posing a significant challenge for traditional forecasting methods. We introduce a Resolution-Aware Retrieval-Augmented Forecasting model that enhances predictive accuracy by leveraging spatial correlations and temporal frequency characteristics. By decomposing signals into different frequency components, our model employs resolution-aware retrieval, where lower-frequency components rely on broader spatial context, while higher-frequency components focus on local influences. This allows the model to dynamically retrieve relevant data and adapt to new locations with minimal historical context. Applied to microclimate forecasting, our model significantly outperforms traditional forecasting methods, numerical weather prediction models, and modern foundation time series models, achieving 71% lower MSE than HRRR and 34% lower MSE than Chronos on the ERA5 dataset. Our results highlight the effectiveness of retrieval-augmented and resolution-aware strategies, offering a scalable and data-efficient solution for zero-shot forecasting in microclimate modeling and beyond.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation. Builds on 16 of its 37 references that are in the map. First posted on arXiv 19 Oct 2025.

Builds on

16 of 37 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024Retrieval Augmented Time Series Forecasting12 Nov

Other references · 15

  1. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
  5. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  6. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 239
  7. 2021Multiwavelet-based Operator Learning for Differential Equationscited by 234
  8. 2023SatCLIP: Global, General-Purpose Location Embeddings with Satellite Imagerycited by 151
  9. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentationcited by 99
  10. 2020Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Datacited by 82
  11. 2021Domain Adaptation for Time Series Forecasting via Attention Sharingcited by 68
  12. 2023Domain Adaptation for Time Series Under Feature and Label Shiftscited by 60
3 more
  1. 2021Long-Range Transformers for Dynamic Spatiotemporal Forecastingcited by 46
  2. 2022Domain Adaptation for Time-Series Classification to Mitigate Covariate Shiftcited by 24
  3. 2022A Novel Transformer Network with Shifted Window Cross-Attention for Spatiotemporal Weather Forecastingcited by 5

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  4. 2026Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters
  5. 2026Super-Resolving Coarse-Resolution Weather Forecasts With Flow Matching
  6. 2026Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting
  7. 2026GRAM: Correcting Frozen Time-Series Foundation Models via Graph-Retrieved Amplitude Memory
  8. 2026Stationarity-Aware Retrieval-Augmented Time Series Forecasting