Detecting Systematic Anomalies Affecting Systems When Inputs Are Stationary Time Series
Applied Stochastic Models in Business and Industry
54 Pages Posted: 9 Jan 2021 Last revised: 11 Sep 2022
Date Written: November 20, 2020
We develop an anomaly-detection method when systematic anomalies, possibly statistically very similar to genuine inputs, 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 random sequences, thus opening up possibilities for diverse applications. To illustrate how the method works on data, and how to interpret its results and make decisions, we analyze several actual time series, which are originally non-stationary but in the process of analysis are converted into stationary. As a further illustration, we provide a controlled experiment with anomaly-free inputs following an ARMA time series model under various contamination scenarios.
Keywords: Control systems, anomaly detection, systematic errors, time series.
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