We develop and test machine learning techniques for successfully using past state time
series data and knowledge of a time-dependent system parameter to predict the evolution of
the “climate” associated with the long-term behavior of a non-stationary dynamical system,
where the non-stationary dynamical system is itself unknown. By the term climate, we mean
the statistical properties of orbits rather than their precise trajectories in time. By the term non-
stationary, we refer to systems that are, themselves, varying with time. We show that our …