  Change point detection (CPD) aims to locate abrupt property changes in time
series data. Recent CPD methods demonstrated the potential of using deep
learning techniques, but often lack the ability to identify more subtle changes
in the autocorrelation statistics of the signal and suffer from a high false
alarm rate. To address these issues, we employ an autoencoder-based methodology
with a novel loss function, through which the used autoencoders learn a
partially time-invariant representation that is tailored for CPD. The result is
a flexible method that allows the user to indicate whether change points should
be sought in the time domain, frequency domain or both. Detectable change
points include abrupt changes in the slope, mean, variance, autocorrelation
function and frequency spectrum. We demonstrate that our proposed method is
consistently highly competitive or superior to baseline methods on diverse
simulated and real-life benchmark data sets. Finally, we mitigate the issue of
false detection alarms through the use of a postprocessing procedure that
combines a matched filter and a newly proposed change point score. We show that
this combination drastically improves the performance of our method as well as
all baseline methods.
