用大模型发现时间序列变量间的因果关系，提升预测准确率与可解释性。
Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models
- 通过教师-学生架构让大模型自动挖掘变量间的因果方向。
- 在26个基线方法中表现优异，零样本泛化能力强。
- 适合需要理解变量交互机制的时序预测场景。
大语言模型（LLM）为时序预测提供了融合多模态数据的潜力，但现有方法存在模型架构中角色边缘化、依赖粗粒度统计文本提示、缺乏可解释性等局限。本文提出Augur，一个完全由大模型驱动的时序预测框架，利用LLM的因果推理能力发现并利用协变量间的有向因果关联。Augur采用两阶段师生架构：强大的教师模型通过启发式搜索结合成对因果检验，从时序数据中推断有向因果图；轻量级学生代理则进一步优化图结构，并对高置信度因果关联进行微调，将这些信息编码为丰富文本提示以执行预测。该设计在保持高预测精度的同时，实现了透明且可追溯的变量交互分析。在真实世界数据集上与26个基线方法的广泛实验表明，Augur不仅性能竞争，还具备鲁棒的零样本泛化能力。
原文摘要 · Abstract (English)
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 26 baselines demonstrate that Augur achieves competitive performance and robust zero-shot generalization.
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