Bridging Classification and Reconstruction: Cooperative Time Series Anomaly Detection

arXiv:2605.26193v1 Announce Type: new Abstract: Time series anomaly detection (TSAD) has long been a hot research topic in data mining due to its various applications. Recent studies challenge the effectiveness of popular deep learning methods for TSAD, suggesting their failure in detecting subtle and prolonged anomalies. Outlier Exposure (OE) and Masked Autoencoder (MAE) emerge as two promising paradigms (classification and reconstruction) for solving the above problems. However, OE-based methods are constrained by poor generalization, while MAE-based methods are limited by masking misalignme

Source: arXiv cs.LG — read the full report at the original publisher.

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