ACL 2026 | 时间序列(Time Series)论文总结【LLM，VLM，多模态大模型，预测，异常检测，推理等】

ACL 2026将在2026年7月2日至7日于美国加利福尼亚州圣迭戈（San Diego, California, United States）举行。

本文总结了ACL 2026上有关时间序列（Time Series）的相关论文。共计14篇，其中Main有6篇，Findings有8篇

时间序列Topic：LLM，VLM，多模态大模型，预测，异常检测，推理等

点击文末阅读原文跳转笔者知乎链接（跳转论文链接更方便）

Main

1 Augur: Modeling Covariate Causal Associations in Time Series via Large Language Models

链接：https://aclanthology.org/2026.acl-long.32/

作者：Zhiqing Cui, Binwu Wang, Qingxiang Liu, Yeqiang Wang, Zhengyang Zhou, Yuxuan Liang, Yang Wang

关键词：预测，LLM，因果

2 Inferring Events from Time Series using Language Models

链接：https://aclanthology.org/2026.acl-long.157/

作者：Mingtian Tan, Mike A Merrill, Zachary Gottesman, Tim Althoff, David Evans, Thomas Hartvigsen

关键词：时序事件预测，LLM

3 ZARA: Training-Free Motion Time-Series Reasoning via Evidence-Grounded LLM Agents

链接：https://aclanthology.org/2026.acl-long.684/

作者：Zechen Li, Baiyu Chen, Hao Xue, Flora D. Salim

关键词：时序推理，Agents

4 Is the Attention Matrix Really the Key to Self‑Attention in Multivariate Long‑Term Time Series Forecasting?

链接：https://aclanthology.org/2026.acl-long.853/

作者：Xinyu Li, Kexi Chen, Jiajie Shen, Ying Zheng, Hong Lu, Jin Zhao, Xin Wang

关键词：长时预测，自注意力机制

5 Markovian Linguistic-Temporal Bridge: Unlocking the Potential of LLMs for Time Series Forecasting

链接：https://aclanthology.org/2026.acl-long.1014/

作者：Siming Sun, Kai Zhang, Xuejun Jiang, Wenchao Meng, Qinmin Yang

关键词：预测，LLM，马尔可夫状态转移图

6 TimeSAF: Towards LLM-Guided Semantic Asynchronous Fusion for Time Series Forecasting

链接：https://aclanthology.org/2026.acl-long.1208/

作者：Fan Zhang, Shiming Fan, Hua Wang

关键词：预测，LLM，语义聚合

Findings

7 Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback

链接：https://aclanthology.org/2026.findings-acl.562/

作者：Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian, Qingsong Wen

关键词：异常检测，时序推理

8 CTRL: Control-Based Time Series Forecasting with LLM-Guided Residual Learning

链接：https://aclanthology.org/2026.findings-acl.1104/

作者：Minkyoung Kim, Daeun Ji, Yohan Lee, Beomsoo Kim, Beakcheol Jang

关键词：预测，LLM，残差学习

9 Learning Dynamic Representations and Policies from Multimodal Clinical Time-Series with Informative Missingness

链接：https://aclanthology.org/2026.findings-acl.1313/

作者：Zihan Liang, Ziwen Pan, Ruoxuan Xiong

关键词：多模态医疗时序

10 Probabilistic Depression Detection from Textual Time Series

链接：https://aclanthology.org/2026.findings-acl.1630/

作者：Fabian Schmidt, Seyedehmoniba Ravan, Vladimir Vlassov

关键词：时序推理，临床访谈话语序列，抑郁检测，概率框架

11 LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics

链接：https://aclanthology.org/2026.findings-acl.1636/

作者：Yueyang Ding, HaoPeng Zhang, Rui Dai, Yi Wang, Tianyu Zong, Kaikui Liu, Xiangxiang Chu

关键词：时序推理，VLM

12 CaTS-Bench: Can Language Models Describe Time Series?

链接：https://aclanthology.org/2026.findings-acl.1722/

作者：Luca Zhou, Pratham Yashwante, Marshall Fisher, Alessio Sampieri, Zihao Zhou, Fabio Galasso, Rose Yu

关键词：时序描述，benchmark

13 Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?

链接：https://aclanthology.org/2026.findings-acl.1756/

作者：Zewen Liu, Juntong Ni, Xianfeng Tang, Max SY Lau, Qi He, Wenpeng Yin, Wei Jin

关键词：符号推理，LLM

14 Enhancing Zero-Shot Time Series Forecasting in Off-the-Shelf LLMs via Noise Injection Prompting

链接：https://aclanthology.org/2026.findings-acl.2054/

作者：Xingyou Yin, Ceyao Zhang, Min Hu, Kai Chen

关键词：零样本预测，LLM，分布偏移，噪声注入提示词