The anomaly detection machine learning features use a bespoke amalgamation of different techniques such as clustering, various types of time series decomposition, Bayesian distribution modeling, and correlation analysis. These analytics provide sophisticated real-time automated anomaly detection for time series data.
The machine learning analytics statistically model the time-based characteristics of your data by observing historical behavior and adapting to new data. The model represents a baseline of normal behavior and can therefore be used to determine how anomalous new events are.
Anomaly detection results are written for each bucket span. These results include scores that are aggregated in order to reduce noise and normalized in order to rank the most mathematically significant anomalies.