在输入为固定时间序列时,发现系统异常现象,影响系统 (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.

翻译：当系统性异常现象影响输入和/或输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(即污染前输入)来自一系列广泛的固定随机序列,从而为其应用打开了最多样化的可能性。为了显示该方法如何在数据上运作,以及如何解释结果和作出决定,我们在各种污染情景下对ARMA时间序列后无异常现象输入进行了广泛的数字实验。