Anomaly Detection

Anomaly detection is the process of identifying unexpected items or events in data sets, which differ from the norm. Anomaly detection is often applied on unlabeled data which is known as unsupervised anomaly detection.

When you are building your simulation you are trying to discover unknown unknowns and carefully examine design assumptions. This is a difficult task and it is not always clear what you are looking for. As a result the best place to start is the design a simulation that will validate the existing design assumptions.