Anomaly detection in streaming data: A comparison and evaluation study

Abstract

The detection of anomalies in streaming data faces complexities that make traditional static methods unsuitable due to computational costs and nonstationarity. We test and evaluate eight state of the art algorithms against prominent challenges related to streaming data. Results show insights regarding accuracy, memory-dependency, parameterization, and pre-knowledge exploitation, thus revealing the high impact of some data characteristics to establish a most appropriate algorithm—namely: locality (i.e., whether outlierness is relative to local contexts), relativeness (i.e., if past data defines outlierness), and concept drift (if it is expected, its intensity and frequency). In most applied cases, such factors can be inferred in advance through the use of historical data and domain knowledge. Assuming the viability of the studied methods in terms of time efficiency, this work discloses key findings to achieve optimal designs of streaming data anomaly detection in real-life applications.