Detecting changes in Hilbert space data based on "repeated" and change-aligned principal components

We study a CUSUM (cumulative sums) procedure for the detection of changes in the means of weakly dependent time series within an abstract Hilbert space framework. We use an empirical projection approach via a principal component representation of the data, i.e., we work with the eigenelements of the (long run) covariance operator. This article contributes to the existing theory in two directions: By means of a recent result of Reimherr (2015) we show, on one hand, that the commonly assumed "separation of the leading eigenvalues" for CUSUM procedures can be avoided. This assumption is not a consequence of the methodology but merely a consequence of the usual proof techniques. On the other hand, we propose to consider change-aligned principal components that allow to further reduce common assumptions on the eigenstructure under the alternative. This approach extends directly to multidirectional changes, i.e. changes that occur at different time points and in different directions, by fusing sufficient information on them into the first component. The latter findings are illustrated by a few simulations and compared with existing procedures in a functional data framework.