Detecting systematic anomalies affecting systems when inputs are stationary time series

We develop an anomaly detection method when systematic anomalies are affecting control systems at the input and/or output stages. The method allows anomaly-free inputs (i.e., those before contamination) to originate from a wide class of stationary random sequences, thus opening up the most diverse possibilities for its applications. To show how the method works on data, and how to interpret results and make decisions, we provide an extensive numerical experiment with anomaly-free inputs following ARMA time series under various contamination scenarios.