VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2603.26842v1
• Date: Fri, 27 Mar 2026 08:48:59 GMT
• Title: VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
• Abstract summary: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems.<n>In this paper, we investigate the applicability of large-scale vision models to TSAD.<n>We propose VAN-AD, a novel MAE-based framework for TSAD.
• Score: 17.972690703544764
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series anomaly detection (TSAD) is essential for maintaining the reliability and security of IoT-enabled service systems. Existing methods require training one specific model for each dataset, which exhibits limited generalization capability across different target datasets, hindering anomaly detection performance in various scenarios with scarce training data. To address this limitation, foundation models have emerged as a promising direction. However, existing approaches either repurpose large language models (LLMs) or construct largescale time series datasets to develop general anomaly detection foundation models, and still face challenges caused by severe cross-modal gaps or in-domain heterogeneity. In this paper, we investigate the applicability of large-scale vision models to TSAD. Specifically, we adapt a visual Masked Autoencoder (MAE) pretrained on ImageNet to the TSAD task. However, directly transferring MAE to TSAD introduces two key challenges: overgeneralization and limited local perception. To address these challenges, we propose VAN-AD, a novel MAE-based framework for TSAD. To alleviate the over-generalization issue, we design an Adaptive Distribution Mapping Module (ADMM), which maps the reconstruction results before and after MAE into a unified statistical space to amplify discrepancies caused by abnormal patterns. To overcome the limitation of local perception, we further develop a Normalizing Flow Module (NFM), which combines MAE with normalizing flow to estimate the probability density of the current window under the global distribution. Extensive experiments on nine real-world datasets demonstrate that VAN-AD consistently outperforms existing state-of-the-art methods across multiple evaluation metrics.We make our code and datasets available at https://github.com/PenyChen/VAN-AD.

Related papers

• Exploring Multimodal Prompts For Unsupervised Continuous Anomaly Detection [12.745823680495887]
  We propose an unsupervised continual anomaly detection framework grounded in multimodal prompting.<n> Specifically, we introduce a Continual Multimodal Prompt Memory Bank (CMPMB) that progressively distills and retains normal patterns from both visual and textual domains.<n>We also devise a Defect-Semantic-Guided Adaptive Fusion Mechanism (DSG-AFM) that integrates an Adaptive Normalization Module (ANM) with a Dynamic Fusion Strategy (DFS) to jointly enhance detection accuracy and adversarial robustness.
  arXiv  Detail & Related papers  (2026-03-23T04:33:11Z)
• Towards One-for-All Anomaly Detection for Tabular Data [87.63505963517512]
  Tabular anomaly detection (TAD) is critical in many real-world applications.<n>We propose OFA-TAD, a framework that only requires one-time training on multiple source datasets.<n>Experiments on 34 datasets from 14 domains demonstrate that OFA-TAD achieves superior anomaly detection performance.
  arXiv  Detail & Related papers  (2026-03-15T14:42:05Z)
• Towards Foundation Models for Zero-Shot Time Series Anomaly Detection: Leveraging Synthetic Data and Relative Context Discrepancy [33.68487894996624]
  Time series anomaly detection (TSAD) is a critical task, but developing models that generalize to unseen data remains a major challenge.<n>We introduce textttTimeRCD, a novel foundation model for TSAD built upon a new pre-training paradigm: Relative Context Discrepancy (RCD)<n>We show that textttTimeRCD significantly outperforms existing general-purpose and anomaly-specific foundation models in zero-shot TSAD.
  arXiv  Detail & Related papers  (2025-09-25T14:05:15Z)
• See it, Think it, Sorted: Large Multimodal Models are Few-shot Time Series Anomaly Analyzers [23.701716999879636]
  Time series anomaly detection (TSAD) is becoming increasingly vital due to the rapid growth of time series data. We introduce a pioneering framework called the Time Series Anomaly Multimodal Analyzer (TAMA) to enhance both the detection and interpretation of anomalies.
  arXiv  Detail & Related papers  (2024-11-04T10:28:41Z)
• Analytic Continual Test-Time Adaptation for Multi-Modality Corruption [31.698362898530124]
  Test-Time Adaptation (TTA) enables pre-trained models to bridge the gap between source and target datasets using unlabeled test data.<n>Test-Time Adaptation (TTA) enables pre-trained models to bridge the gap between source and target datasets using unlabeled test data.
  arXiv  Detail & Related papers  (2024-10-29T01:21:24Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• GM-DF: Generalized Multi-Scenario Deepfake Detection [49.072106087564144]
  Existing face forgery detection usually follows the paradigm of training models in a single domain. In this paper, we elaborately investigate the generalization capacity of deepfake detection models when jointly trained on multiple face forgery detection datasets.
  arXiv  Detail & Related papers  (2024-06-28T17:42:08Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series [83.76994646443498]
  In time series anomaly detection, the scarcity of labeled data poses a challenge to the development of accurate models.<n>We propose a novel Domain Contrastive learning model for Anomaly Detection in time series (DACAD)<n>Our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain.
  arXiv  Detail & Related papers  (2024-04-17T11:20:14Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.