Oftentimes in practice, the observed process changes statistical properties at an unknown point in time and the duration of a change is substantially finite, in which case one says that the change is intermittent or transient. We provide an overview of existing approaches for intermittent change detection and advocate in favor of a particular setting driven by the intermittent nature of the change. We propose a novel optimization criterion that is more appropriate for many applied areas such as the detection of threats in physical-computer systems, near-Earth space informatics, epidemiology, pharmacokinetics, etc. We argue that controlling the local conditional probability of a false alarm, rather than the familiar average run length to a false alarm, and maximizing the local conditional probability of detection is a more reasonable approach versus a traditional quickest change detection approach that requires minimizing the expected delay to detection. We adopt the maximum likelihood (ML) approach with respect to the change duration and show that several commonly used detection rules (CUSUM, window-limited CUSUM, and FMA) are equivalent to the ML-based stopping times. We discuss how to choose design parameters for these rules and provide a comprehensive simulation study to corroborate intuitive expectations.

翻译：在实践中，观测到的过程通常会在一个未知时间点改变其统计特性，且这种变化的持续时间是有限的，即所谓间歇性或瞬态变化。本文综述了现有的间歇性变化检测方法，并基于变化的间歇性特性提出了一种特定的分析框架。我们提出了一种新的优化准则，该准则更适合物理-计算机系统中的威胁检测、近地空间信息学、流行病学、药代动力学等多个应用领域。我们认为，控制虚警的局部条件概率（而非传统的虚警平均运行长度）并最大化检测的局部条件概率，相较于需要最小化检测期望延迟的快速变化检测方法更为合理。我们采用针对变化持续时间的极大似然（ML）方法，并证明了几种常用检测规则（CUSUM、窗口限制CUSUM和FMA）等价于基于极大似然的停时。本文讨论了这些规则的设计参数选择方法，并通过全面的仿真研究验证了直观预期。