Anodot Cost monitors cost metrics and identifies anomalies from your cost metrics’ normal behavior patterns, as learned through Anodot’s patented algorithms. These anomalies are listed in the Anomaly Detection anomalies panel, which is accessed from the Monitoring > Anomaly Detection menu.
In order to form the metric baseline you'll need a minimum of one-month historical data on a running system.
What are anomalies?
Anomalies are deviations from a normal pattern in one or more metrics that signal unexpected behavior. Anomalies are not, by definition, good or bad. They are simply unexpected results.
Anodot’s machine learning algorithms automatically track metrics to determine an expected pattern. The result is a normal range, referred to as the baseline for each metric. As long as the metric value is within the learned normal range, no anomalies are detected.
The normal range for each metric is determined using Anodot’s patent algorithms and is based on many factors that are automatically detected, such as metric category (stationary/non-stationary, discrete, sparse, and more), detected seasonal patterns, and trends.