Empirical Investigation into Configuring Echo State Networks for Representative Benchmark Problem Domains

2025-08-14 · Brooke R. Weborg, Gursel Serpen arxiv

This paper examines Echo State Network, a reservoir computer, performance using four different benchmark problems, then proposes heuristics or rules of thumb for configuring the architecture, as well as the selection of parameters and their values, which are applicable to problems within the same domain, to help serve to fill the experience gap needed by those entering this field of study. The influence of various parameter selections and their value adjustments, as well as architectural changes made to an Echo State Network, a powerful recurrent neural network configured as a reservoir computer, can be challenging to fully comprehend without experience in the field, and even some hyperparameter optimization algorithms may have difficulty adjusting parameter values without proper manual selections made first. Therefore, it is imperative to understand the effects of parameters and their value selection on Echo State Network architecture performance for a successful build. Thus, to address the requirement for an extensive background in Echo State Network architecture, as well as examine how Echo State Network performance is affected with respect to variations in architecture, design, and parameter selection and values, a series of benchmark tasks representing different problem domains, including time series prediction, pattern generation, chaotic system prediction, and time series classification, were modeled and experimented on to show the impact on the performance of Echo State Network.

📄 PDF Abstract BibTeX arXiv:2508.10887

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Hyperparameter Optimization Time Series Classification Time Series Prediction

Reservoir Topology in Deep Echo State Networks

2019-09-24 · Claudio Gallicchio, Alessio Micheli

Deep Echo State Networks (DeepESNs) recently extended the applicability of Reservoir Computing (RC) methods towards the field of deep learning. In this paper we study the impact of constrained reservoir topologies in the…

Optimal Neuron Selection: NK Echo State Networks for Reinforcement Learning

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This paper introduces the NK Echo State Network. The problem of learning in the NK Echo State Network is reduced to the problem of optimizing a special form of a Spin Glass Problem known as an NK Landscape. No weight adj…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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We study the uniform approximation of echo state networks with randomly generated internal weights. These models, in which only the readout weights are optimized during training, have made empirical success in learning d…

From Many Models, One: Macroeconomic Forecasting with Reservoir Ensembles

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Time Series Forecasting

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Language models are achieving impressive performance on various tasks by aggressively adopting inference-time prompting techniques, such as zero-shot and few-shot prompting. In this work, we introduce EchoPrompt, a simpl…

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