mlsanityData to 14 Oct 2026

Harnessing Vision-Language Models for Time Series Anomaly Detection

arXiv 2506.06836 ↗First posted 7 Jun 2025cs.CV cs.AI cs.LGcited by 11

On the map: Multimodal LLMs / vision-language models → Chart understanding and anomaly detection

Abstract (arXiv)

Time-series anomaly detection (TSAD) has played a vital role in a variety of fields, including healthcare, finance, and sensor-based condition monitoring. Prior methods, which mainly focus on training domain-specific models on numerical data, lack the visual-temporal understanding capacity that human experts have to identify contextual anomalies. To fill this gap, we explore a solution based on vision language models (VLMs). Recent studies have shown the ability of VLMs for visual understanding tasks, yet their direct application to time series has fallen short on both accuracy and efficiency. To harness the power of VLMs for TSAD, we propose a two-stage solution, with (1) ViT4TS, a vision-screening stage built on a relatively lightweight pre-trained vision encoder, which leverages 2D time series representations to accurately localize candidate anomalies; (2) VLM4TS, a VLM-based stage that integrates global temporal context and VLM's visual understanding capacity to refine the detection upon the candidates provided by ViT4TS. We show that without any time-series training, VLM4TS outperforms time-series pre-trained and from-scratch baselines in most cases, yielding a 24.6% improvement in F1-max score over the best baseline. Moreover, VLM4TS also consistently outperforms existing language model-based TSAD methods and is on average 36x more efficient in token usage.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Placed in Chart understanding and anomaly detection, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 33 of its 45 references that are in the map. First posted on arXiv 7 Jun 2025.

Builds on

33 of 45 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Can Multimodal LLMs Perform Time Series Anomaly Detection?25 Feb

Other references · 32

  1. 2023Visual Instruction Tuningcited by 7,869
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  3. 2021LAION-400M: Open Dataset of CLIP-Filtered 400 Million Image-Text Pairscited by 1,555
  4. 2024Chronos: Learning the Language of Time Seriescited by 759
  5. 2023A decoder-only foundation model for time-series forecastingcited by 692
  6. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 482
  7. 2018Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholdingcited by 410
  8. 2021Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancycited by 394
  9. 2023WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentationcited by 315
  10. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 287
  11. 2021VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localizationcited by 223
  12. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
20 more
  1. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  2. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  3. 2023Multimodal Large Language Models: A Surveycited by 195
  4. 2022Deep Learning for Time Series Anomaly Detection: A Surveycited by 131
  5. 2024UniTS: A Unified Multi-Task Time Series Modelcited by 94
  6. 2015Evaluating Real-time Anomaly Detection Algorithms - the Numenta Anomaly Benchmarkcited by 88
  7. 2023Time Series as Images: Vision Transformer for Irregularly Sampled Time Seriescited by 79
  8. 2020TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networkscited by 55
  9. 2024Can LLMs Understand Time Series Anomalies?cited by 41
  10. 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
  11. 2024UniMTS: Unified Pre-training for Motion Time Seriescited by 26
  12. 2025Harnessing Vision Models for Time Series Analysis: A Surveycited by 26
  13. 2024See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzerscited by 20
  14. 2025Achieving Time Series Reasoning Requires Rethinking Model Design, Tasks Formulation, and Evaluationcited by 17
  15. 2024Zero-Shot Visual Reasoning by Vision-Language Models: Benchmarking and Analysiscited by 15
  16. 2024From Pixels to Predictions: Spectrogram and Vision Transformer for Better Time Series Forecastingcited by 9
  17. 2022AER: Auto-Encoder with Regression for Time Series Anomaly Detectioncited by 8
  18. 2024Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Modelscited by 4
  19. 2023Your time series is worth a binary image: machine vision assisted deep framework for time series forecastingcited by 3
  20. 2025M2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated Thresholdingcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 9.

11 papers.

  1. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  2. 2026ARFBench: Benchmarking Time Series Question Answering Ability for Software Incident Responsecited by 6
  3. 2025AXIS: Explainable Time Series Anomaly Detection with Large Language Modelscited by 4
  4. 2026OpenMHC: Accelerating the Science of Wearable Foundation Modelscited by 2
  5. 2026NeuroCanvas: VLLM-Powered Robust Seizure Detection by Reformulating Multichannel EEG as Imagecited by 0
  6. 2026AnomSeer: Reinforcing Multimodal LLMs to Reason for Time-Series Anomaly Detectioncited by 0
  7. 2026VETime: Vision Enhanced Zero-Shot Time Series Anomaly Detectioncited by 0
  8. 2026VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detectioncited by 0
  9. 2026DAST: A VLM-LLM Framework for Cross-Interface Anomaly Detection in O-RANcited by 0
  10. 2026A Picture is Worth a Thousand Tokens: How Vision Language Models Cut AI Energy Costs While Improving Accuracycited by 0
  11. 2026Do More Modalities Always Help? A Geometric Perspective on Missing-Modality Robustnesscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025ViTs: Teaching Machines to See Time Series Anomalies Like Human Experts
  2. 2026Tiny but Trusted: Efficient Vision-Language Reasoning for Time-Series Anomaly Detection
  3. 2024Quo Vadis, Anomaly Detection? LLMs and VLMs in the Spotlight
  4. 2024Video Anomaly Detection in 10 Years: A Survey and Outlook
  5. 2024Vision-Language Models Assisted Unsupervised Video Anomaly Detection
  6. 2025Refining Time Series Anomaly Detectors using Large Language Models
  7. 2024Large Language Models can Deliver Accurate and Interpretable Time Series Anomaly Detection
  8. 2025From Benchmarks to Reality: Advancing Visual Anomaly Detection by the VAND 3.0 Challenge