SVM-Based Anomaly Detectors

Our more advanced Generative AI anomaly detectors don't work for every application - in some cases, the problem is actually too simple for gen-AI to be effective.

For these situations, we offer anomaly detection using support vector machines.

SVMs are simple AI models that are extremely fast to train and run. They are used mainly to detect outliers in data, which means they're ideally suited to identifying abnormal values.

The only major draw-back with support vector machines is how simple their output is. SVMs simply give a score showing you how 'common' the current asset state is. There's no asking why its flagging an anomaly - it just tells you that there is one.

To be used as an anomaly detector, your SVM needs a threshold. This is a score that is used to tell the difference between a good value and an anomalous one.

The system will automatically pick a value that it things might be accurate, but you may choose to tune it (ie. by reducing or increasing it slightly) if you find that you're getting false positives.

Increasing the value will make your model more sensitive, meaning you'll see more anomalies being detected. Decreasing the value will make the system accept more variation in value, reducing the number of detections.

This is done the same way it's done in generative AIs - by pressing the Thresholds button and choosing new values.