Abstract: Systems and devices for the evaluation and analysis of computing system data for anomaly detection and processing are disclosed. In an example, operations to detect anomalies include: obtaining a source event stream of data produced from operation of a computing system; converting the source event stream into a frequency signal; identifying an estimated seasonality from the frequency signal; scaling the estimated seasonality to a target event stream produced from operation of the computing system; identifying anomalies of a principal vector of the target event stream, based on deviation from the estimated seasonality; and causing the computing system to perform an action based on the identified anomalies. In a further example, the operations include scaling the estimated seasonality to a subset of the target event stream indicating minor vectors; and identifying anomalies of the minor vectors, based on deviation of the subset of data from the estimated seasonality.

Abstract: Systems and devices for the evaluation and analysis of computing system data for anomaly detection and processing are disclosed. In an example, operations to detect anomalies include: obtaining a source event stream of data produced from operation of a computing system; converting the source event stream into a frequency signal; identifying an estimated seasonality from the frequency signal; scaling the estimated seasonality to a target event stream produced from operation of the computing system; identifying anomalies of a principal vector of the target event stream, based on deviation from the estimated seasonality; and causing the computing system to perform an action based on the identified anomalies. In a further example, the operations include scaling the estimated seasonality to a subset of the target event stream indicating minor vectors; and identifying anomalies of the minor vectors, based on deviation of the subset of data from the estimated seasonality.