train_esn: Train an Echo State Network

In ahaeusser/echos: Echo State Networks for Time Series Modeling and Forecasting

Train an Echo State Network

Description

Train an Echo State Network (ESN) to a univariate time series. The function automatically manages data pre-processing, reservoir generation (i.e., internal states) and model estimation and selection.

Usage

train_esn(
  y,
  lags = 1,
  inf_crit = "bic",
  n_diff = NULL,
  n_states = NULL,
  n_models = NULL,
  n_initial = NULL,
  n_seed = 42,
  alpha = 1,
  rho = 0.95,
  density = 0.5,
  lambda = c(1e-04, 2),
  scale_win = 0.5,
  scale_wres = 0.5,
  scale_inputs = c(-0.5, 0.5)
)

Arguments

y	Numeric vector containing the response variable.
lags	Integer vector with the lag(s) associated with the input variable.
inf_crit	Character value. The information criterion used for variable selection inf_crit = c("aic", "aicc", "bic").
n_diff	Integer vector. The nth-differences of the response variable.
n_states	Integer value. The number of internal states per reservoir.
n_models	Integer value. The maximum number of (random) models to train for model selection.
n_initial	Integer value. The number of observations of internal states for initial drop out (throw-off).
n_seed	Integer value. The seed for the random number generator (for reproducibility).
alpha	Numeric value. The leakage rate (smoothing parameter) applied to the reservoir.
rho	Numeric value. The spectral radius for scaling the reservoir weight matrix.
density	Numeric value. The connectivity of the reservoir weight matrix (dense or sparse).
lambda	Numeric vector. Lower and upper bound of lambda sequence for ridge regression.
scale_win	Numeric value. The lower and upper bound of the uniform distribution for scaling the input weight matrix.
scale_wres	Numeric value. The lower and upper bound of the uniform distribution for scaling the reservoir weight matrix.
scale_inputs	Numeric vector. The lower and upper bound for scaling the time series data.

Value

A list containing:

• actual: Numeric vector containing the actual values.

• fitted: Numeric vector containing the fitted values.

• resid: Numeric vector containing the residuals.

• states_train: Numeric matrix containing the internal states.

• method: A list containing several objects and meta information of the trained ESN (weight matrices, hyperparameters, model metrics, etc.).

Examples

xdata <- as.numeric(AirPassengers)
xmodel <- train_esn(y = xdata)
summary(xmodel)

ahaeusser/echos documentation built on June 2, 2025, 2:17 a.m.