  Under adversarial attacks, time series regression and classification are
vulnerable. Adversarial defense, on the other hand, can make the models more
resilient. It is important to evaluate how vulnerable different time series
models are to attacks and how well they recover using defense. The sensitivity
to various attacks and the robustness using the defense of several time series
models are investigated in this study. Experiments are run on seven-time series
models with three adversarial attacks and one adversarial defense. According to
the findings, all models, particularly GRU and RNN, appear to be vulnerable.
LSTM and GRU also have better defense recovery. FGSM exceeds the competitors in
terms of attacks. PGD attacks are more difficult to recover from than other
sorts of attacks.
