DATA-AGNOSTIC ANOMALY DETECTION

This disclosure presents computational systems and methods for detecting anomalies in data output from any type of monitoring tool. The data is aggregated and sent to an alerting system for abnormality detection via comparison with normalcy bounds. The anomaly detection methods are performed by construction of normalcy bounds of the data based on the past behavior of the data output from the monitoring tool. The methods use data quality assurance and data categorization processes that allow choosing a correct procedure for determination of the normalcy bounds. The methods are completely data agnostic, and as a result, can also be used to detect abnormalities in time series data associated with any complex system.