  We address the problem of output prediction, ie. designing a model for
autonomous nonlinear systems capable of forecasting their future observations.
We first define a general framework bringing together the necessary properties
for the development of such an output predictor. In particular, we look at this
problem from two different viewpoints, control theory and data-driven
techniques (machine learning), and try to formulate it in a consistent way,
reducing the gap between the two fields. Building on this formulation and
problem definition, we propose a predictor structure based on the
Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well
into our general framework. Finally, we propose a constructive solution for
this predictor that solely relies on a small set of trajectories measured from
the system. Our experiments show that our solution allows to obtain an
efficient predictor over a subset of the observation space.
