论文标题
当输入为固定时间序列时，检测影响系统的系统异常
Detecting systematic anomalies affecting systems when inputs are stationary time series
论文作者
论文摘要
当系统异常（可能与真实输入非常相似）会影响输入和/或输出阶段的控制系统时，我们会开发出一种异常检测方法。该方法允许无异常的输入（即污染前的输入）源自一系列随机序列，从而为各种应用打开了可能性。为了说明该方法如何在数据上工作，以及如何解释其结果并做出决策，我们分析了几个实际时间序列，这些时间序列最初是非平稳的，但在分析过程中被转换为固定。作为进一步的例证，我们在各种污染方案下，在ARMA时间序列模型之后提供了无异常输入的对照实验。
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.
