Non-stationarity affects the sensitivity of change detection in correlated systems described by sets of measurable variables. We study this by projecting onto different principal components. Non-stationarity is modeled as multiple normal states that exist in the system even before a change occurs. The studied changes occur in mean values, standard deviations or correlations of the variables. Monte Carlo simulations are performed to test the sensitivity for change detection with and without knowledge about the non-stationarity for different system dimensions and numbers of normal states. A comparison clearly shows that the knowledge about the non-stationarity of the system greatly improves change detection sensitivity for all principal components. This improvement is largest for those components that already provide the greatest possibility for change detection in the stationary case. We illustrate our results with an example using real traffic flow data, in which we detect a weekend and a bank holiday start as anomalies.

翻译：非平稳性会影响通过可测变量集合描述的相关系统中变化检测的灵敏度。我们通过将系统投影到不同的主成分上来研究这一问题。非平稳性被建模为变化发生前系统中存在的多个正常状态。所研究的变化发生在变量的均值、标准差或相关性上。采用蒙特卡洛模拟来测试在已知和未知非平稳性的情况下，针对不同系统维度和正常状态数量的变化检测灵敏度。比较结果清晰表明，对系统非平稳性的了解显著提高了所有主成分的变化检测灵敏度。这种提升在平稳情况下已提供最大变化检测可能性的主成分上最为显著。我们通过一个实际交通流量数据的示例来展示结果，成功检测到周末和法定节假日开始作为异常事件。