Wassertein GAN synthesis for time series with complex temporal dynamics: Frugal architectures and arbitrary sample-size generation

Generating surrogate data using Deep Neural Network (DNN) has become a classic task in image processing, while DNN time series synthesis is less often considered. The present work addresses issues related to the DNN synthesis of time series, with complex, scalefree time nonreversible temporal dynamics, using Wassertein Generative Adversarial Network. Instead of proposing yet another overperforming architecture, it discusses, first, synthesis quality quantitative assessment and, second, architecture designs that both reduce, for the Generator, the number of trainable parameters by a factor of $10000$ (compared to state-of-the-art architectures), at no expense in performance cost, and permit to generate time series of size longer than that of the training set, without retraining. This works can thus be considered a contribution towards sustainable Artificial Intelligence.