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Echo state networks are computationally lightweight reservoir models inspiredby the random projections observed in cortical circuitry. As interest inreservoir computing has grown, networks have become deeper and more intricate.While these networks are increasingly applied to nontrivial forecasting tasks,there is a need for comprehensive performance analysis of deep reservoirs. Inthis work, we study the influence of partitioning neurons given a budget andthe effect of parallel reservoir pathways across different datasets exhibitingmulti-scale and nonlinear dynamics.

Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting