Retrieval Augmented Time Series Forecasting

Sungwon Han, Seungeon Lee, Meeyoung Cha, Sercan O Arik, Jinsung Yoon
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:21774-21797, 2025.

Abstract

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%.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-han25d, title = {Retrieval Augmented Time Series Forecasting}, author = {Han, Sungwon and Lee, Seungeon and Cha, Meeyoung and Arik, Sercan O and Yoon, Jinsung}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {21774--21797}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/han25d/han25d.pdf}, url = {https://proceedings.mlr.press/v267/han25d.html}, abstract = {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%.} }
Endnote
%0 Conference Paper %T Retrieval Augmented Time Series Forecasting %A Sungwon Han %A Seungeon Lee %A Meeyoung Cha %A Sercan O Arik %A Jinsung Yoon %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-han25d %I PMLR %P 21774--21797 %U https://proceedings.mlr.press/v267/han25d.html %V 267 %X 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%.
APA
Han, S., Lee, S., Cha, M., Arik, S.O. & Yoon, J.. (2025). Retrieval Augmented Time Series Forecasting. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:21774-21797 Available from https://proceedings.mlr.press/v267/han25d.html.

Related Material