Publications Details

Automated EWMA Anomaly Detection Pipeline

Gilletly, Samuel G.; Cauthen, Katherine R.; Mott, Joshua; Brown, Nathanael J.

There is a need to perform offline anomaly detection in count data streams to simultaneously identify both systemic changes and outliers, simultaneously. We propose a new algorithmic method, called the Anomaly Detection Pipeline, which leverages common statistical process control procedures in a novel way to accomplish this. The method we propose does not require user-defined control or phase I training data, automatically identifying regions of stability for improved parameter estimation to support change point detection. The method does not require data to be normally distributed, and it detects outliers relative to the regimes in which they occur. Our proposed method performs comparably to state-of-the-art change point detection methods, provides additional capabilities, and is extendable to a larger set of possible data streams than known methods.