Improving the prediction of spatio-temporal chaos by combining parallel reservoir computing with dimensionality reduction

Abstract

Reservoir computers can be used to predict time series generated by spatio-temporal chaotic systems. Using multiple reservoirs in parallel has shown improved performances for these predictions, by effectively reducing the input dimensionality of each reservoir. Similarly, one may further reduce the dimensionality of the input data by transforming to a lower-dimensional latent space. Combining both approaches, we show that using dimensionality-reduced latent space predictions for parallel reservoir computing not only reduces computational costs, but also leads to better prediction results for small to medium reservoir sizes. In the combined approach we further demonstrate that dimensionality reduction improves small-reservoir predictions regardless of noise contaminating the training data. The benefit of dimensionality-reduced parallel reservoir computing is illustrated and evaluated on the basis of the prediction of the one-dimensional Kuramoto-Sivashinsky equation.