Forecast an Echo State Network (ESN) from a trained model via recursive forecasting. 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.
Usage
forecast_esn(
object,
n_ahead = 18,
levels = c(80, 95),
n_sim = 100,
n_seed = 42
)
Arguments
- object
An object of class
esn
. The result of a call totrain_esn()
.- n_ahead
Integer value. The number of periods for forecasting (i.e. forecast horizon).
- levels
Integer vector. The levels of the forecast intervals, e.g., 80% and 95%.
- n_sim
Integer value. The number of future sample path generated during simulation.
- n_seed
Integer value. The seed for the random number generator (for reproducibility).
Value
A list
containing:
point
: Numeric vector containing the point forecasts.interval
: Numeric matrix containing the forecast intervals.sim
: Numeric matrix containing the simulated future sample path.levels
: Integer vector. The levels of the forecast intervals.actual
: Numeric vector containing the actual values.fitted
: Numeric vector containing the fitted values.n_ahead
: Integer value. The number of periods for forecasting (forecast horizon).model_spec
: Character value. The model specification as string.
Examples
xdata <- as.numeric(AirPassengers)
xmodel <- train_esn(y = xdata)
xfcst <- forecast_esn(xmodel, n_ahead = 12)
plot(xfcst)