Systems and methods for anomaly detection

Abstract

The present disclosure describes systems and methods that provide a hybrid framework for augmenting statistical anomaly detection with contextual features, machine learning and human Subject Matter Expert (SME) input to learn significant characteristics of true anomalies for which alerts should be generated. The framework presented herein is domain agnostic and independent of the underlying statistical anomaly detection technique or the machine learning algorithm. The framework described herein is therefore applicable and adaptable to a number of real world service provider systems and applications, such as, for example, detecting network performance degradation in a service provider network or detecting anomalous conditions from data received from a sensor while filtering out false positives.