nnetar(x, p, P=1, size, repeats=20, lambda=NULL)
## S3 method for class 'nnetar':
forecast(object, h=ifelse(object$m > 1, 2 * object$m, 10),
lambda=object$lambda, ...)
nnetar
generated by nnetar
.nnetar
returns an object of class "nnetar
". forecast.nnetar
returns an object of class "forecast
".The function summary
is used to obtain and print a summary of the
results, while the function plot
produces a plot of the forecasts.
The generic accessor functions fitted.values
and residuals
extract useful features of the value returned by nnetar
.
An object of class "forecast"
is a list containing at least the following elements:
object
itself or the time series used to create the model stored as object
).x
as inputs and a single hidden layer with size
nodes. The inputs are for lags 1 to p
, and lags m
to mP
where m=frequency(x)
. A total of repeats
networks are fitted, each with random starting weights. These are then averaged when computing forecasts. The network is trained for one-step forecasting. Multi-step forecasts are computed recursively. The fitted model is called an NNAR(p,P) model and is analogous to an ARIMA(p,0,0)(P,0,0) model but with nonlinear functions.fit <- nnetar(lynx)
fcast <- forecast(fit)
plot(fcast)
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