mlsanityData to 14 Oct 2026

Retrieval Augmented Time Series Forecasting

arXiv 2505.04163 ↗First posted 7 May 2025cs.LG cs.IRcited by 5

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

Abstract (arXiv)

Time series forecasting uses historical data to predict future trends, leveraging the relationships between past observations and available features. In this paper, we propose RAFT, a retrieval-augmented time series forecasting method to provide sufficient inductive biases and complement the model's learning capacity. When forecasting the subsequent time frames, we directly retrieve historical data candidates from the training dataset with patterns most similar to the input, and utilize the future values of these candidates alongside the inputs to obtain predictions. This simple approach augments the model's capacity by externally providing information about past patterns via retrieval modules. Our empirical evaluations on ten benchmark datasets show that RAFT consistently outperforms contemporary baselines with an average win ratio of 86%.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Placed in Time series and agricultural RAG, one of 15 directions in Retrieval-augmented generation. Builds on 28 of its 44 references that are in the map. First posted on arXiv 7 May 2025.

Builds on

28 of 44 references.

References · 28

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  3. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  4. 2020REALM: Retrieval-Augmented Language Model Pre-Trainingcited by 2,141
  5. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  6. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  7. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  8. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  9. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
  10. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  11. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  12. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
16 more
  1. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  2. 2023A decoder-only foundation model for time-series forecastingcited by 692
  3. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 614
  4. 2019Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecastingcited by 607
  5. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 391
  6. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  7. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 332
  8. 2019Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directionscited by 172
  9. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 160
  10. 2017Conditional Time Series Forecasting with Convolutional Neural Networkscited by 123
  11. 2021Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learningcited by 113
  12. 2024SparseTSF: Modeling Long-term Time Series Forecasting with 1k Parameterscited by 107
  13. 2023TabR: Tabular Deep Learning Meets Nearest Neighbors in 2023cited by 74
  14. 2024A Survey of Deep Learning and Foundation Models for Time Series Forecastingcited by 33
  15. 2020Few-shot Learning for Time-series Forecastingcited by 16
  16. 2022DNNR: Differential Nearest Neighbors Regressioncited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Chain-of-thought Reviewing and Correction for Time Series Question Answeringcited by 1
  2. 2026RareCP: Regime-Aware Retrieval for Efficient Conformal Predictioncited by 1
  3. 2025STRATA-TS: Selective Knowledge Transfer for Urban Time Series Forecasting with Retrieval-Guided Reasoningcited by 0
  4. 2026READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosiscited by 0
  5. 2026AdaCast: Conditional Parameter Generation for Adaptive Time Series Forecastingcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025QuiZSF: A Retrieval-Augmented Framework for Zero-Shot Time Series Forecasting
  2. 2026Semantics-Enhanced Retrieval-Augmented Time Series Forecasting
  3. 2026Stationarity-Aware Retrieval-Augmented Time Series Forecasting
  4. 2024Retrieval Augmented Time Series Forecasting
  5. 2026Which Histories Matter for Time Series Forecasting? Learning Predictive Relevance with Future Supervision
  6. 2026Channel-wise Retrieval for Multivariate Time Series Forecasting
  7. 2026Model-agnostic Retrieval-Augmented Extended Forecasting for time series
  8. 2026Retrieval Is Not Enough: Refreshing Memory for Frozen Time-Series Forecasters