domain-anomaly-detection

Use for anomaly, outlier, and novelty detection across data types: industrial or visual defect detection, time-series or sensor anomalies and predictive maintenance, tabular fraud or intrusion detection, and out-of-distribution detection. Usually one-class or unsupervised. Picks the method by data type, sets the threshold honestly, and uses imbalance-aware metrics. Triggers on 'anomaly detection', 'outlier', 'defect detection', 'novelty', 'fraud', 'intrusion', 'out of distribution', 'predictive maintenance', 'one-class'.