Robustness and generalization performance of Deep Learning Models on Cyber-Physical Systems

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.
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