Outlier Detection Models are the simplest form of anomaly detector.

Unlike value-predicting models, this type of AI simply gives a simple reading to say 'this is normal' or 'this is unusual'.

Unfortunately, they aren't able to explain why they are indicating an anomaly - they only let you know that the data coming from the asset(s) is unexpected.

Generally, these types of AI generate a value - you can adjust exactly how sensitive your anomaly detector is to changes by choosing the threshold the system uses to go from saying 'OK' to 'Anomalous'.

A high threshold will result in a less sensitive anomaly detector.

These sorts of models are ideal for when the assets….

• Don't have very much data,
  
• Have no context information, or
  
• Have significant gaps in context information

They are simple, relatively un-complicated, and work in situations where there simply isn't enough usable data to predict what values should be.

The AI Anomaly Detector addon uses Support Vector Machines to provide this style of anomaly detection. See the differences between model types for more information.