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

TL;DR

This study empirically investigates how to configure Echo State Networks for various benchmark problems, providing heuristics and insights to guide parameter and architecture choices for practitioners.

Contribution

It offers new heuristics and guidelines for configuring Echo State Networks tailored to different problem domains, filling a knowledge gap for practitioners.

Findings

Parameter and architecture choices significantly impact performance.

Heuristics improve configuration efficiency across domains.

Experimental results demonstrate the effectiveness of proposed guidelines.

Abstract

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.