  Reservoir computing is a best-in-class machine learning algorithm for
processing information generated by dynamical systems using observed
time-series data. Importantly, it requires very small training data sets, uses
linear optimization, and thus requires minimal computing resources. However,
the algorithm uses randomly sampled matrices to define the underlying recurrent
neural network and has a multitude of metaparameters that must be optimized.
Recent results demonstrate the equivalence of reservoir computing to nonlinear
vector autoregression, which requires no random matrices, fewer metaparameters,
and provides interpretable results. Here, we demonstrate that nonlinear vector
autoregression excels at reservoir computing benchmark tasks and requires even
shorter training data sets and training time, heralding the next generation of
reservoir computing.
