VAN-AD adapts a pretrained visual MAE with distribution mapping and normalizing flow modules to detect anomalies in time series data more effectively across different datasets.
Discovering cluster-based local outliers,
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Extending Matrix Profile to multidimensional time series yields the only method among 19 baselines that maintains high anomaly detection performance across unsupervised, supervised, and semi-supervised regimes on 119 datasets.
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VAN-AD: Visual Masked Autoencoder with Normalizing Flow For Time Series Anomaly Detection
VAN-AD adapts a pretrained visual MAE with distribution mapping and normalizing flow modules to detect anomalies in time series data more effectively across different datasets.
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Matrix Profile for Anomaly Detection on Multidimensional Time Series
Extending Matrix Profile to multidimensional time series yields the only method among 19 baselines that maintains high anomaly detection performance across unsupervised, supervised, and semi-supervised regimes on 119 datasets.