Breaking Boundaries: Balancing Performance and Robustness in Deep Wireless Traffic Forecasting. (arXiv:2311.09790v2 [cs.LG] UPDATED)

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 …

accuracy algorithms breaking challenge data forecasting hard performance presence robustness series trade traffic wireless