@inproceedings{yang-etal-2026-time,
    title = "Time-{RA}: Towards Time Series Reasoning for Anomaly Diagnosis with {LLM} Feedback",
    author = "Yang, Yiyuan  and
      Liu, Zichuan  and
      Song, Lei  and
      Ying, Kai  and
      Wang, Stephen  and
      Bamford, Joshua Thomas  and
      Vyetrenko, Svitlana  and
      Bian, Jiang  and
      Wen, Qingsong",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.562/",
    pages = "11591--11616",
    ISBN = "979-8-89176-395-1",
    abstract = "Time series anomaly detection (TSAD) has traditionally focused on binary classification and often lacks the fine-grained categorization and explanatory reasoning required for transparent decision-making. To address these limitations, we propose Time-series Reasoning for Anomaly (Time-RA), a novel task that reformulates TSAD from a discriminative into a generative, reasoning-intensive paradigm. To facilitate this, we introduce RATs40K, the first real-world large-scale multimodal benchmark with {\textasciitilde}40,000 samples across 10 domains, integrating raw time series, textual context, and visual plots with structured reasoning annotations. Extensive benchmarking shows that while supervised fine-tuning and visual representations boost diagnostic accuracy and reasoning consistency, performance varies across complex scenarios. Notably, fine-tuned models demonstrate strong ``plug-and-play'' transferability, outperforming traditional baselines on unseen real-world datasets. Our work establishes a foundation for interpretable, multimodal time series analysis. All code and the RATs40K dataset are fully open-sourced to facilitate future research."
}