Inferring the fractional nature of Wu Baleanu trajectories

Abstract

We infer the parameters of fractional discrete Wu Baleanu time series by using machine learning architectures based on recurrent neural networks. Our results shed light on how clearly one can determine that a given trajectory comes from a specific fractional discrete dynamical system by estimating the fractional exponent and the growth parameter mu . With this example, we also show how machine learning methods can be incorporated into the study of fractional dynamical systems.