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formula argument in
ESN() for the fabletools model interface.m4_data to m4_monthly_subset to
clarify that the dataset contains a monthly subset of the M4 competition
data. The old name m4_data is retained for backward
compatibility.m4_monthly_subset and
synthetic_data.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.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.