  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.
