# Dimensional criterion for forecasting nonlinear systems by reservoir computing

arXiv: https://arxiv.org/abs/2202.05159 · HTML: https://mlsanity.com/paper/2202.05159/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 27 references that are in the map.
- First posted on arXiv 9 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Forecasting Chaotic Systems with Very Low Connectivity Reservoir Computers](https://mlsanity.com/paper/1910.00659/) (2019)
- [Dimension of Reservoir Computers](https://mlsanity.com/paper/1912.06472/) (2019)
- [Learning Continuous Chaotic Attractors with a Reservoir Computer](https://mlsanity.com/paper/2110.08631/) (2021)

## Built on by (most cited first, 1 of 1)

- [Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing](https://mlsanity.com/paper/2310.09433/) (2023)

## Abstract (arXiv)

Reservoir computers (RC) have proven useful as surrogate models in forecasting and replicating systems of chaotic dynamics. The quality of surrogate models based on RCs is crucially dependent on their optimal implementation that involves selecting optimal reservoir topology and hyperparameters. By systematically applying Bayesian hyperparameter optimization and using ensembles of reservoirs of various topology we show that connectednes of reservoirs is of significance only in forecasting and replication of chaotic system of sufficient complexity. By applying RCs of different topology in forecasting and replicating the Lorenz system, a coupled Wilson-Cowan system, and the Kuramoto-Sivashinsky system, we show that simple reservoirs of unconnected nodes (RUN) outperform reservoirs of connected nodes for target systems whose estimated fractal dimension dimension is d lesssim 5.5 and that linked reservoirs are better for systems with d > 5.5. This finding is highly important for evaluation of reservoir computing methods and on selecting a method for prediction of signals measured on nonlinear systems.