Anomaly Detection
Anomaly detection identifies data points, events, or patterns that deviate significantly from normal behavior. In industrial applications, it detects equipment degradation before failure.
Types of Anomalies
- Point anomalies — a single data point is abnormal
- Contextual anomalies — a data point is abnormal in context
- Collective anomalies — a group of data points is abnormal together
Methods
- Statistical — deviation from statistical distribution
- Machine learning — autoencoders, isolation forests, one-class SVM
- Deep learning — LSTM autoencoders for time-series data
- Hybrid — combining multiple methods for robustness
VLTHRLAB's Approach
VLTHRLAB uses LSTM autoencoders for time-series anomaly detection, achieving AUC up to 1.000 on 8 benchmark datasets with zero false alarms on cross-dataset transfer.