Assessing Deep Generative Models on Time-Series Network Data
preprintposted on 21.04.2022, 14:11 by Muhammad Haris NaveedMuhammad Haris Naveed, Umair Hashmi, Nayab Tajved, Neha Sultan, Ali Imran
This paper explores whether Generative Adversarial Networks (GANs) can produce realistic network load data that can be utilized to train machine learning models in lieu of real data. In this regard, we evaluate the performance of three recent GAN architectures on the Telecom Italia data set across a set of qualitative and quantitative metrics. Our results show that GAN generated synthetic data is indeed similar to real data and forecasting models trained on this data achieve similar performance to those trained on real data.