mlsanityData to 7 Oct 2026

Can LLMs Serve As Time Series Anomaly Detectors?

arXiv 2408.03475 ↗First posted 6 Aug 2024cs.LG cs.AIcited by 22

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Abstract (arXiv)

An emerging topic in large language models (LLMs) is their application to time series forecasting, characterizing mainstream and patternable characteristics of time series. A relevant but rarely explored and more challenging question is whether LLMs can detect and explain time series anomalies, a critical task across various real-world applications. In this paper, we investigate the capabilities of LLMs, specifically GPT-4 and LLaMA3, in detecting and explaining anomalies in time series. Our studies reveal that: 1) LLMs cannot be directly used for time series anomaly detection. 2) By designing prompt strategies such as in-context learning and chain-of-thought prompting, GPT-4 can detect time series anomalies with results competitive to baseline methods. 3) We propose a synthesized dataset to automatically generate time series anomalies with corresponding explanations. By applying instruction fine-tuning on this dataset, LLaMA3 demonstrates improved performance in time series anomaly detection tasks. In summary, our exploration shows the promising potential of LLMs as time series anomaly detectors.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 23 of its 37 references that are in the map. First posted on arXiv 6 Aug 2024.

Builds on

23 of 37 references.

References · 23

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2022Training language models to follow instructions with human feedbackcited by 15,712
  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  5. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
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  7. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  8. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  9. 2023Visual Instruction Tuningcited by 7,792
  10. 2022Training Compute-Optimal Large Language Modelscited by 2,927
  11. 2022Emergent Abilities of Large Language Modelscited by 2,483
  12. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
11 more
  1. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  2. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 863
  3. 2022A Survey on In-context Learningcited by 717
  4. 2024Chronos: Learning the Language of Time Seriescited by 712
  5. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  6. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  7. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  8. 2023LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasterscited by 127
  9. 2024LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Promptingcited by 56
  10. 2023Large Language Models for Spatial Trajectory Patterns Miningcited by 27
  11. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 16; 2026: 4.

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  1. 2025Multi-Agent Collaboration Mechanisms: A Survey of LLMscited by 424
  2. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  3. 2024A Picture is Worth A Thousand Numbers: Enabling LLMs Reason about Time Series via Visualizationcited by 31
  4. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  5. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  6. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  7. 2025Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Modelscited by 17
  8. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?cited by 11
  9. 2025Inferring Events from Time Series using Language Modelscited by 10
  10. 2025BALM-TSF: Balanced Multimodal Alignment for LLM-Based Time Series Forecastingcited by 5
  11. 2025Toxicity Begets Toxicity: Unraveling Conversational Chains in Political Podcastscited by 4
  12. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Modelscited by 4
10 more
  1. 2025Foundation Models for Anomaly Detection: Vision and Challengescited by 3
  2. 2026AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoningcited by 3
  3. 2025Text-ADBench: Text Anomaly Detection Benchmark Based on LLM Embeddingscited by 1
  4. 2025Robustness is Important: Limitations of LLMs for Data Fittingcited by 1
  5. 2026LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detectioncited by 1
  6. 2026Detecting Time Series Anomalies Like an Expert: A Multi-Agent LLM Framework with Specialized Analyzerscited by 1
  7. 2025Towards Time Series Generation Conditioned on Unstructured Natural Languagecited by 0
  8. 2025Human-AI Collaborative Bot Detection in MMORPGscited by 0
  9. 2025Solve Smart, Not Often: Policy Learning for Costly MILP Re-solvingcited by 0
  10. 2026VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detectioncited by 0

Similar, not cited

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  1. 2025When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
  2. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
  3. 2025Refining Time Series Anomaly Detectors using Large Language Models
  4. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detection
  5. 2025Toward Reasoning-Centric Time-Series Analysis
  6. 2024Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment
  7. 2025RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detection
  8. 2026Rethinking the Role of LLMs in Time Series Forecasting