mlsanityData to 7 Oct 2026

Can LLMs Understand Time Series Anomalies?

arXiv 2410.05440 ↗First posted 7 Oct 2024cs.LGcited by 41

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Abstract (arXiv)

Large Language Models (LLMs) have gained popularity in time series forecasting, but their potential for anomaly detection remains largely unexplored. Our study investigates whether LLMs can understand and detect anomalies in time series data, focusing on zero-shot and few-shot scenarios. Inspired by conjectures about LLMs' behavior from time series forecasting research, we formulate key hypotheses about LLMs' capabilities in time series anomaly detection. We design and conduct principled experiments to test each of these hypotheses. Our investigation reveals several surprising findings about LLMs for time series: (1) LLMs understand time series better as images rather than as text, (2) LLMs do not demonstrate enhanced performance when prompted to engage in explicit reasoning about time series analysis. (3) Contrary to common beliefs, LLMs' understanding of time series does not stem from their repetition biases or arithmetic abilities. (4) LLMs' behaviors and performance in time series analysis vary significantly across different models. This study provides the first comprehensive analysis of contemporary LLM capabilities in time series anomaly detection. Our results suggest that while LLMs can understand trivial time series anomalies, we have no evidence that they can understand more subtle real-world anomalies. Many common conjectures based on their reasoning capabilities do not hold. All synthetic dataset generators, final prompts, and evaluation scripts have been made available in https://github.com/rose-stl-lab/anomllm.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). Builds on 32 of its 46 references that are in the map. First posted on arXiv 7 Oct 2024.

Builds on

32 of 46 references.

References · 32

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,767
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  6. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  7. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  8. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,179
  9. 2024MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmarkcited by 1,758
  10. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  11. 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suitescited by 1,033
  12. 2019VideoBERT: A Joint Model for Video and Language Representation Learningcited by 702
20 more
  1. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  2. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
  3. 2024Long-context LLMs Struggle with Long In-context Learningcited by 285
  4. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 211
  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. 2023How well do Large Language Models perform in Arithmetic tasks?cited by 130
  8. 2023Large language models can accurately predict searcher preferencescited by 129
  9. 2024ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generationcited by 118
  10. 2024Large Language Models for Time Series: A Surveycited by 113
  11. 2022Local Evaluation of Time Series Anomaly Detection Algorithmscited by 79
  12. 2024Time Series Forecasting with LLMs: Understanding and Enhancing Model Capabilitiescited by 48
  13. 2024Position: What Can Large Language Models Tell Us about Time Series Analysiscited by 47
  14. 2024Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detectioncited by 26
  15. 2023Leveraging Vision-Language Models for Granular Market Change Predictioncited by 22
  16. 2024Can LLMs Serve As Time Series Anomaly Detectors?cited by 22
  17. 2022Do Deep Neural Networks Contribute to Multivariate Time Series Anomaly Detection?cited by 19
  18. 2024Do Language Models Exhibit the Same Cognitive Biases in Problem Solving as Human Learners?cited by 19
  19. 2023Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstructioncited by 17
  20. 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: 2024: 1; 2025: 25; 2026: 15.

41 papers.

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  1. 2026TimeART: Towards Agentic Time Series Reasoning via Tool-Augmentationcited by 25
  2. 2025Multi-modal Time Series Analysis: A Tutorial and Surveycited by 24
  3. 2025Foundation Models for Spatio-Temporal Data Science: A Tutorial and Surveycited by 23
  4. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  5. 2025How Can Time Series Analysis Benefit From Multiple Modalities? A Survey and Outlookcited by 20
  6. 2025BCAT: A Block Causal Transformer for PDE Foundation Models for Fluid Dynamicscited by 16
  7. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluationcited by 15
  8. 2026TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Modelscited by 14
  9. 2025SciTS: Scientific Time Series Understanding and Generation with LLMscited by 13
  10. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?cited by 11
  11. 2025Inferring Events from Time Series using Language Modelscited by 10
  12. 2025Harnessing Vision-Language Models for Time Series Anomaly Detectioncited by 9
29 more
  1. 2025Multimodal Conditioned Diffusive Time Series Forecastingcited by 8
  2. 2026TSAQA: Time Series Analysis Question And Answering Benchmarkcited by 8
  3. 2026HEARTS: Benchmarking LLM Reasoning on Health Time Seriescited by 8
  4. 2025From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?cited by 7
  5. 2025Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Modelscited by 7
  6. 2025Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedbackcited by 7
  7. 2025CaTS-Bench: Can Language Models Describe Time Series?cited by 7
  8. 2026ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Responsecited by 5
  9. 2025Large Language Model Powered Intelligent Urban Agents: Concepts, Capabilities, and Applicationscited by 4
  10. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Modelscited by 4
  11. 2025Towards Interpretable Time Series Foundation Modelscited by 3
  12. 2026Time Series Reasoning via Process-Verifiable Thinking Data Synthesis and Scheduling for Tailored LLM Reasoningcited by 3
  13. 2026AnomaMind: Agentic Time Series Anomaly Detection with Tool-Augmented Reasoningcited by 3
  14. 2025Refining Time Series Anomaly Detectors using Large Language Modelscited by 2
  15. 2025Are Large Language Models Capable of Deep Relational Reasoning? Insights from DeepSeek-R1 and Benchmark Comparisonscited by 2
  16. 2024DDD-GenDT: Dynamic Data-driven Generative Digital Twin Frameworkcited by 1
  17. 2025Rating Quality of Diverse Time Series Data by Meta-learning from LLM Judgmentcited by 1
  18. 2025RATFM: Retrieval-augmented Time Series Foundation Model for Anomaly Detectioncited by 1
  19. 2025The Forecast Critic: Leveraging Large Language Models for Poor Forecast Identificationcited by 1
  20. 2026LLM-Assisted Logic Rule Learning: Scaling Human Expertise for Time Series Anomaly Detectioncited by 1
  21. 2026ASTER: Latent Pseudo-Anomaly Generation for Unsupervised Time-Series Anomaly Detectioncited by 1
  22. 2025BEAR: BGP Event Analysis and Reportingcited by 0
  23. 2025From Time Series Analysis to Question Answering: A Survey in the LLM Eracited by 0
  24. 2026Rationale-Grounded In-Context Learning for Time Series Reasoning with Multimodal Large Language Modelscited by 0
  25. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detectioncited by 0
  26. 2026KairosVL: Orchestrating Time Series and Semantics for Unified Reasoningcited by 0
  27. 2026VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detectioncited by 0
  28. 2026Speaking Numbers to LLMs: Multi-Wavelet Number Embeddings for Time Series Forecastingcited by 0
  29. 2026Cardiologent: Multi-Agent Clinical Decision Support for Patient-Level Arrhythmia Assessment, Urgency, and Managementcited by 0

Similar, not cited

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  1. 2025Toward Reasoning-Centric Time-Series Analysis
  2. 2024Understanding Why Large Language Models Can Be Ineffective in Time Series Analysis: The Impact of Modality Alignment
  3. 2025When LLM Meets Time Series: Can LLMs Perform Multi-Step Time Series Reasoning and Inference
  4. 2026Rethinking the Role of LLMs in Time Series Forecasting
  5. 2026Representing Time Series as Structured Programs for LLM Reasoning
  6. 2026TimeSeriesExamAgent: Creating Time Series Reasoning Benchmarks at Scale
  7. 2025TsLLM: Augmenting LLMs for General Time Series Understanding and Prediction
  8. 2026LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications