  Recent research has established the effectiveness of machine learning for
data-driven prediction of the future evolution of unknown dynamical systems,
including chaotic systems. However, these approaches require large amounts of
measured time series data from the process to be predicted. When only limited
data is available, forecasters are forced to impose significant model structure
that may or may not accurately represent the process of interest. In this work,
we present a Meta-learning Approach to Reservoir Computing (MARC), a
data-driven approach to automatically extract an appropriate model structure
from experimentally observed "related" processes that can be used to vastly
reduce the amount of data required to successfully train a predictive model. We
demonstrate our approach on a simple benchmark problem, where it beats the
state of the art meta-learning techniques, as well as a challenging chaotic
problem.
