Detecting changes in metric values

Obviously, not all data being emitted from systems will be text or categorical in nature—a vast amount of it is numerical. Detecting changes in metric values over time is perfectly suited for anomaly detection because, as mentioned in Chapter 1, Machine Learning for IT, the historical paradigm of alerting on exceptions in numerical values via static thresholds has been troublesome for decades. Let's explore all that Elastic ML has to offer with respect to the functions that help you detect changes in numerical fields in your data.

Metric functions

Metric functions operate on numerical fields and return numerical values. They are perhaps the easiest of the detector functions to understand.

min, max, mean, median, and metric

These functions do exactly as you would expect: they return the minimum, maximum, average/mean, and median of all of the numerical observations for the field of interest in the bucket span.

The metric...