@inproceedings{cui-etal-2026-augur,
title = "Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models",
author = "Cui, Zhiqing and
Wang, Binwu and
Liu, Qingxiang and
Wang, Yeqiang and
Zhou, Zhengyang and
Liang, Yuxuan and
Wang, Yang",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.32/",
pages = "764--787",
ISBN = "979-8-89176-390-6",
abstract = "Large language models (LLM) have emerged as a promising avenue for time series forecasting, offering the potential to integrate multimodal data. However, existing LLM-based approaches face notable limitations{---}such as marginalized role in model architectures, reliance on coarse statistical text prompts, and lack of interpretability. In this work, we introduce Augur, a fully LLM driven time series forecasting framework that exploits LLM causal reasoning to discover and use directed causal associations among covariates. Augur uses a two stage teacher student architecture where a powerful teacher LLM infers a directed causal graph from time series using heuristic search together with pairwise causality testing. A lightweight student agent then refines the graph and fine tune on high confidence causal associations that are encoded as rich textual prompts to perform forecasting. This design improves predictive accuracy while yielding transparent, traceable reasoning about variable interactions. Extensive experiments on real-world datasets with 25 baselines demonstrate that Augur achieves competitive performance and robust zero-shot generalization."
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%0 Conference Proceedings
%T Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models
%A Cui, Zhiqing
%A Wang, Binwu
%A Liu, Qingxiang
%A Wang, Yeqiang
%A Zhou, Zhengyang
%A Liang, Yuxuan
%A Wang, Yang
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F cui-etal-2026-augur
%X Large language models (LLM) have emerged as a promising avenue for time series forecasting, offering the potential to integrate multimodal data. However, existing LLM-based approaches face notable limitations—such as marginalized role in model architectures, reliance on coarse statistical text prompts, and lack of interpretability. In this work, we introduce Augur, a fully LLM driven time series forecasting framework that exploits LLM causal reasoning to discover and use directed causal associations among covariates. Augur uses a two stage teacher student architecture where a powerful teacher LLM infers a directed causal graph from time series using heuristic search together with pairwise causality testing. A lightweight student agent then refines the graph and fine tune on high confidence causal associations that are encoded as rich textual prompts to perform forecasting. This design improves predictive accuracy while yielding transparent, traceable reasoning about variable interactions. Extensive experiments on real-world datasets with 25 baselines demonstrate that Augur achieves competitive performance and robust zero-shot generalization.
%U https://aclanthology.org/2026.acl-long.32/
%P 764-787
Markdown (Informal)
[Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models](https://aclanthology.org/2026.acl-long.32/) (Cui et al., ACL 2026)
ACL
- Zhiqing Cui, Binwu Wang, Qingxiang Liu, Yeqiang Wang, Zhengyang Zhou, Yuxuan Liang, and Yang Wang. 2026. Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 764–787, San Diego, California, United States. Association for Computational Linguistics.