Multivariate Anomaly Detection - Training the Model

What is multivariate anomaly detection?

Multivariate anomaly detection is the advanced form of anomaly detection where the machine learning algorithm needs to learn multiple features that can cause an anomaly in the system. It means that there is a dependency on more than one feature which can cause an anomaly. All the features are associated with a particular timestamp. For example, to determine the anomalies in an environment of a room, multiple features could be involved like temperature, humidity, oxygen level, and so on.


We need to follow the steps mentioned in the below illustration to implement multivariate anomaly detection with Azure anomaly detector service.

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