  Deep learning (DL) models have seen increased attention for time series
forecasting, yet the application on cyber-physical systems (CPS) is hindered by
the lacking robustness of these methods. Thus, this study evaluates the
robustness and generalization performance of DL architectures on multivariate
time series data from CPS. Our investigation focuses on the models' ability to
handle a range of perturbations, such as sensor faults and noise, and assesses
their impact on overall performance. Furthermore, we test the generalization
and transfer learning capabilities of these models by exposing them to
out-of-distribution (OOD) samples. These include deviations from standard
system operations, while the core dynamics of the underlying physical system
are preserved. Additionally, we test how well the models respond to several
data augmentation techniques, including added noise and time warping. Our
experimental framework utilizes a simulated three-tank system, proposed as a
novel benchmark for evaluating the robustness and generalization performance of
DL algorithms in CPS data contexts. The findings reveal that certain DL model
architectures and training techniques exhibit superior effectiveness in
handling OOD samples and various perturbations. These insights have significant
implications for the development of DL models that deliver reliable and robust
performance in real-world CPS applications.
