tau to
train_esn(), enabling dynamic control of reservoir
size.tune_esn() to tune hyperparameters
alpha, rho and tau via time
series cross-validation (i.e., rolling forecasts).summary.tune_esn() and
plot.tune_esn() to summarize and visualize results from
hyperparameter tuning.train_esn() so n_initial is only
auto-set when NULL.y and inf_crit
in train_esn() and levels in
forecast_esn().forecast_esn(),
forecast.ESN() and plot.forecast_esn().
Forecast intervals are generated by simulating future sample path based
on a moving block bootstrap of the residuals and estimating the
quantiles from the simulations.plot.esn() to visualize the internal states
(i.e., the reservoir).filter_esn() to extract ESN models from a
mable.synthetic_data, a dataset with synthetic time
series data as tibble.
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