  In this paper, we present a novel algorithm to optimize the design of
Reservoir Computing using Cellular Automata models for time series
applications. Besides selecting the models' hyperparameters, the proposed
algorithm particularly solves the open problem of linear Cellular Automaton
rule selection. The selection method pre-selects only a few promising candidate
rules out of an exponentially growing rule space. When applied to relevant
benchmark datasets, the selected rules achieve low errors, with the best rules
being among the top 5% of the overall rule space. The algorithm was developed
based on mathematical analysis of linear Cellular Automaton properties and is
backed by almost one million experiments, adding up to a computational runtime
of nearly one year. Comparisons to other state-of-the-art time series models
show that the proposed Reservoir Computing using Cellular Automata models have
lower computational complexity, at the same time, achieve lower errors. Hence,
our approach reduces the time needed for training and hyperparameter
optimization by up to several orders of magnitude.
