Learnability of chaotic nature of generalized Boole transformations with GANs

Abstract

Generative Adversarial Networks (GANs) are the architecture of significant interest in the field of data generation using machine learning. Attempts have been made in the past to generate time-series data using GANs. In this study, we generated time series following a generalized Boole transformation using GANs and investigated how well the generated data preserved the characteristics of the original time series from both a dynamic and statistical perspective. Additionally, we examined the impact of the distribution followed by the random noise input to the GAN’s generator on the pseudo time series.