====Creating AI Anomaly Detector Models====

The process of creating a model for our AI anomaly detector is mostly automatic - you only need to provide two things.

1) Any properties we should ignore, and \\
2) Example times where the asset has been behaving 'normally'.

===Properties to Ignore===

There are some properties that shouldn't be included in anomaly detector models. These include...

//Points with Radically Different Time-Scales//

If you're detecting anomalies using properties that change every second, you should avoid mixing in properties that only change **daily** (such as daily performance metrics and KPIs) unless you're willing to include large amounts of sample data, as false-positives are common when mixing data across time-scales.

//Points Already Powered By Anomaly Detectors//

If you've already made an anomaly detector for this asset and are using it live, you should make sure you exclude that point from your AI. Otherwise you end up with a **circular dependency** - a situation where your AI needs data from **itself** to function.

===Normal Operation Examples===

By default, the system will take a snapshot of the last 2 hours of time as 'normal'. But you can define your own set of times you use as examples of normal operation for the asset.

//Normal// might also include situations where...

* The asset is switched off, \\
* The asset is on, but unutilised, \\
* The asset is operating in extreme environmental conditions \\

===Creation Walkthrough===

[[walkthrough|Read our walkthrough on creating an anomaly detector]].