ReLiCADA -- Reservoir Computing using Linear Cellular Automata Design Algorithm
This work addresses a specific bottleneck in time series modeling for researchers and practitioners, offering incremental improvements in efficiency and accuracy.
The paper tackles the problem of optimizing Reservoir Computing with Cellular Automata for time series by solving the open issue of linear Cellular Automaton rule selection, achieving low errors with rules in the top 5% of the rule space and reducing training time by up to several orders of magnitude compared to state-of-the-art models.
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.