Anomaly Detection
The process of identifying data points, events, or observations that deviate significantly from expected patterns.
How It Works
Anomaly detection algorithms learn what 'normal' looks like from historical data, then flag anything that deviates. Techniques include statistical methods (z-score, IQR), isolation forests, autoencoders, and clustering-based approaches.
Applications
Fraud detection in banking, network intrusion detection, manufacturing quality control, medical diagnostics, and predictive maintenance. Any domain where rare but important events need to be caught.
Challenges
Anomalies are rare by definition, creating class imbalance. What counts as 'anomalous' can shift over time (concept drift). False positives can be costly, requiring careful threshold tuning.