Papers by Qingxiang Liu
Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models (2026.acl-long)
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| Challenge: | Large language models (LLMs) have emerged as a promising avenue for time series forecasting . existing approaches face limitations such as marginalized role in model architectures and lack of interpretability. |
| Approach: | They propose a framework that exploits LLM causal reasoning to discover and use directed causal associations among covariates. |
| Outcome: | The proposed model improves predictive accuracy while yielding transparent, traceable reasoning about variable interactions. |