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In this paper, we present a novel algorithm to optimize the design ofReservoir Computing using Cellular Automata models for time seriesapplications. Besides selecting the models' hyperparameters, the proposedalgorithm particularly solves the open problem of linear Cellular Automatonrule selection. The selection method pre-selects only a few promising candidaterules out of an exponentially growing rule space. When applied to relevantbenchmark datasets, the selected rules achieve low errors, with the best rulesbeing among the top 5% of the overall rule space. The algorithm was developedbased on mathematical analysis of linear Cellular Automaton properties and isbacked by almost one million experiments, adding up to a computational runtimeof nearly one year. Comparisons to other state-of-the-art time series modelsshow that the proposed Reservoir Computing using Cellular Automata models havelower computational complexity, at the same time, achieve lower errors. Hence,our approach reduces the time needed for training and hyperparameteroptimization by up to several orders of magnitude.

ReLiCADA -- Reservoir Computing using Linear Cellular Automata Design Algorithm