Generative Adversarial Networks (GAN) are proved effective for generating synthetic data. However, they fall behind when it comes to generating time-series data, which is observed in various real-world systems that deal with scheduling, load balancing, congestion control, etc. This work analyses how different GAN architectures, based on RNN, CNN, and transformers, can be used to generate time-series datasets with various characteristics. Throughout the experiments, the paper gives insights into which GAN architecture best fits each dataset type. All the reviewers acknowledged the importance of the problem and agreed on the usefulness of the study.

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