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.

翻译：当系统性异常现象(在统计上可能与真正的投入非常相似)正在影响输入和/或产出阶段的控制系统时,我们开发了一种异常现象检测方法。该方法允许无异常现象输入(即污染前的输入)来自一系列广泛的随机序列,从而为多种应用开辟了可能性。为了说明该方法如何在数据上发挥作用,以及如何解释其结果和作出决定,我们分析了几个实际的时间序列,这些时间序列原本是非静止的,但在分析过程中是静止的。进一步说明,我们根据各种污染情景下的ARMA时间序列模型,对无异常现象输入进行了控制实验。