论文标题
主要组件对系统变化的敏感性在存在非平稳性的情况下
Sensitivity of principal components to system changes in the presence of non-stationarity
论文作者
论文摘要
非平稳性会影响通过可测量变量集所描述的相关系统中变化检测的敏感性。我们通过投影到不同的主要组件来研究这一点。即使在更改发生之前，非平稳性也被建模为系统中存在的多个正常状态。研究的变化发生在变量的平均值，标准偏差或相关性中。进行蒙特卡洛模拟，以测试有关不同系统维度和正常状态数量的非平稳性的变化检测的敏感性。比较清楚地表明，有关系统非平稳性的知识极大地提高了所有主要组件的变化检测敏感性。对于那些已经为固定案例提供了最大可能性检测可能性的组件而言，这种改进是最大的。我们使用真实的交通流数据来说明结果，其中我们发现一个周末，而银行假期开始为异常。
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.
