EchoStateNetwork

Echo state networks / reservoir computing in pure numpy. One class, EchoStateNetwork: ridge-regression training with chaotic recycle validation (contiguous runs or ragged dwell segments), parametric inputs, optional Bayesian hyperparameter search (scikit-optimize), closed-loop prediction, and Jacobians for data assimilation.

This is the single shared reservoir core behind qlrom (quantized-local ESN families) and romda (real-time ESN forecasting and bias-aware data assimilation).

Documentation: https://andreanovoa.github.io/EchoStateNetwork/

Tutorials

• tutorials/01_echo_state_network.ipynb — train an ESN on the Lorenz 63 system, with full and partial observability.
• tutorials/02_parametric_esn.ipynb — one reservoir conditioned on a physical parameter, forecasting at unseen parameter values.

Install

pip install echostatenetwork          # core (numpy/scipy/matplotlib)
pip install "echostatenetwork[opt]"   # + scikit-optimize for Bayesian hyperparameter search
pip install -e ".[dev]"               # development

Quickstart

import numpy as np
from echostatenetwork import EchoStateNetwork

y = my_time_series                       # (Nt, N_dim)
esn = EchoStateNetwork(y, dt=0.01, N_units=200, t_train=40.0, t_val=4.0)
esn.train([y])
u_wash, r = ...                          # see docstrings: washout then closed loop

Acknowledgements

This repository is based on alberacca/Echo-State-Networks, the reference implementation of the recycle-validation ESN:

Racca, A., & Magri, L. (2021). Robust optimization and validation of echo state networks for learning chaotic dynamics. Neural Networks, 142, 252-268. doi:10.1016/j.neunet.2021.05.004