  We focus on chaotic dynamical systems and analyze their time series with the
use of autoencoders, i.e., configurations of neural networks that map identical
output to input. This analysis results in the determination of the latent space
dimension of each system and thus determines the minimal number of nodes
necessary to capture the essential information contained in the chaotic time
series. The constructed chaotic autoencoders generate similar maximal Lyapunov
exponents as the original chaotic systems and thus encompass their essential
dynamical information.
