mlsanityData to 7 Oct 2026

Refining Time Series Anomaly Detectors using Large Language Models

arXiv 2503.21833 ↗First posted 26 Mar 2025cs.CLcited by 2

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Abstract (arXiv)

Time series anomaly detection (TSAD) is of widespread interest across many industries, including finance, healthcare, and manufacturing. Despite the development of numerous automatic methods for detecting anomalies, human oversight remains necessary to review and act upon detected anomalies, as well as verify their accuracy. We study the use of multimodal large language models (LLMs) to partially automate this process. We find that LLMs can effectively identify false alarms by integrating visual inspection of time series plots with text descriptions of the data-generating process. By leveraging the capabilities of LLMs, we aim to reduce the reliance on human effort required to maintain a TSAD system

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 14 of its 18 references that are in the map. First posted on arXiv 26 Mar 2025.

Builds on

14 of 18 references.

References · 14

  1. 2024The Llama 3 Herd of Modelscited by 14,192
  2. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  3. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  4. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 387
  5. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 163
  6. 2020Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progresscited by 148
  7. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 95
  8. 2024Language Models Still Struggle to Zero-shot Reason about Time Seriescited by 81
  9. 2024TimeSeriesExam: A time series understanding examcited by 47
  10. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  11. 2024Large language models can be zero-shot anomaly detectors for time series?cited by 30
  12. 2022Unsupervised Model Selection for Time-series Anomaly Detectioncited by 24
2 more
  1. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzerscited by 20
  2. 2024Position: Quo Vadis, Unsupervised Time Series Anomaly Detection?cited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2026AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoningcited by 3
  2. 2025PB-IAD: Utilizing multimodal foundation models for semantic industrial anomaly detection in dynamic manufacturing environmentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?
  2. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
  3. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Models
  4. 2024Can LLMs Serve As Time Series Anomaly Detectors?
  5. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
  6. 2026Evaluating Large Language Models for Time Series Anomaly Detection in Aerospace Software
  7. 2025Harnessing Vision-Language Models for Time Series Anomaly Detection
  8. 2026ChatAD: Reasoning-Enhanced Time-Series Anomaly Detection with Multi-Turn Instruction Evolution