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

Paper · 2026 · Yiyuan Yang, Zichuan Liu, Lei Song et al.

Summary

Conventional time series anomaly detection (TSAD) usually just labels data as normal or anomalous. It does not say what kind of anomaly occurred or why. The authors propose TIME-RA, which recasts TSAD as a generative reasoning problem. A (multimodal) LLM must detect an anomaly, assign it to a fine-grained category, and explain its decision through Observation, Thought and Action steps.

To support this task they release RATs40K, a real-world benchmark of roughly 40,000 samples from 10 domains. Each sample combines raw series, textual context and rendered plots. A pool of strong LLMs produced the first annotations, and a GPT-4-based feedback loop then refined them.

Benchmarking several (M)LLMs shows three things:

  • Supervised fine-tuning improves both accuracy and reasoning consistency.
  • Visual input also improves both.
  • Results remain uneven in harder scenarios.

A fine-tuned Qwen2.5-7B transfers to unseen datasets without retraining, which suggests practical use where labels are scarce. This work offers a basis for interpretable, multimodal anomaly diagnosis.

Key points

  • Taxonomy: RATs40K's category scheme covers 14 anomaly kinds for univariate series and 6 for multivariate series, and every category comes with a formal definition and illustrative scenarios.
  • Inputs: each sample has three modalities: the numeric time series, domain text and a visual plot.
  • Transfer results: applied as-is, the fine-tuned Qwen2.5-7B beats KNN, LOF and AE1SVM. It stays competitive with TimesNet, Anomaly Transformer, Timer and Chronos, with very high recall on the NEK, GHL, SED and CATSv2 datasets. XGBoost still leads in precision.
  • Ablations: few-shot examples and Chain-of-Thought prompting improve reasoning coherence and categorization accuracy.
  • Availability and limitations: code is on GitHub (yyysjz1997/Time-RA) and data on Hugging Face (Time-RA/RATs40K). The framework predicts only one dominant category per sequence and struggles with very long inputs.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of OxfordOxford · United Kingdom104 works · 157,183 citations🎓Nanjing UniversityNanjing · China74 works · 7,814 citations🎓Université de StrasbourgStrasbourg · France9 works · 2,814 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksSignal Processing · 452 worksComputer Networks and Communications · 64 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3,320 worksReasoning · 1,426 works

Cite

Cite as Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian and Qingsong Wen. 2026. Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.findings-acl.562

@inproceedings{yang2026time,
  title = {{Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback}},
  author = {Yiyuan Yang, Zichuan Liu, Lei Song, Kai Ying, Stephen Wang, Joshua Thomas Bamford, Svitlana Vyetrenko, Jiang Bian and Qingsong Wen},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.562},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.562}
}

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