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.

在时间序列预测中，准确性和鲁棒性之间的平衡是一个长期存在的挑战。本文研究了一系列的扰动情景，并提出了一种基于真实的电信数据的对抗攻击的新型防御机制。通过将分类器用于检测对抗样本，降噪器用于消除扰动数据样本中的噪声以及标准预测器的组合，我们的混合策略在干净和扰动数据上表现出最佳性能。同时，在正常和扰动数据上，我们的优化模型相对于基准方法在均方差方面的性能提高了2.71倍和2.51倍，且组件间可以并行训练，提高了计算效率。我们的结果表明，即使在复杂的破坏性攻击条件下，通过改进分类器和降噪器，我们仍然可以在性能和鲁棒性之间取得最优的平衡。

突破界限：在深层网络流量预测中平衡性能和稳健性