  Reservoir computers (RC) are a form of recurrent neural network (RNN) used
for forecasting timeseries data. As with all RNNs, selecting the
hyperparameters presents a challenge when training onnew inputs. We present a
method based on generalized synchronization (GS) that gives direction in
designing and evaluating the architecture and hyperparameters of an RC. The
'auxiliary method' for detecting GS provides a computationally efficient
pre-training test that guides hyperparameterselection. Furthermore, we provide
a metric for RC using the reproduction of the input system's Lyapunov
exponentsthat demonstrates robustness in prediction.
