  Balancing the trade-off between accuracy and robustness is a long-standing
challenge in time series forecasting. While most of existing robust algorithms
have achieved certain suboptimal performance on clean data, sustaining the same
performance level in the presence of data perturbations remains extremely hard.
In this paper, we study a wide array of perturbation scenarios and propose
novel defense mechanisms against adversarial attacks using real-world telecom
data. We compare our strategy against two existing adversarial training
algorithms under a range of maximal allowed perturbations, defined using
$\ell_{\infty}$-norm, $\in [0.1,0.4]$. Our findings reveal that our hybrid
strategy, which is composed of a classifier to detect adversarial examples, a
denoiser to eliminate noise from the perturbed data samples, and a standard
forecaster, achieves the best performance on both clean and perturbed data. Our
optimal model can retain up to $92.02\%$ the performance of the original
forecasting model in terms of Mean Squared Error (MSE) on clean data, while
being more robust than the standard adversarially trained models on perturbed
data. Its MSE is 2.71$\times$ and 2.51$\times$ lower than those of comparing
methods on normal and perturbed data, respectively. In addition, the components
of our models can be trained in parallel, resulting in better computational
efficiency. Our results indicate that we can optimally balance the trade-off
between the performance and robustness of forecasting models by improving the
classifier and denoiser, even in the presence of sophisticated and destructive
poisoning attacks.
