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.