VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
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-d...
| Authors | PengYu Chen, Shang Wan, Xiaohou Shi et al. |
|---|---|
| Primary category | cs.LG |
| Published | 27 March 2026 |
| Last updated at source | 16 July 2026 |
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