Returns forecasts and other information for user-defined models.
# S3 method for modelAR
forecast(
object,
h = ifelse(object$m > 1, 2 * object$m, 10),
PI = FALSE,
level = c(80, 95),
fan = FALSE,
xreg = NULL,
lambda = object$lambda,
bootstrap = FALSE,
npaths = 1000,
innov = NULL,
...
)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 and
prediction intervals.
The generic accessor functions fitted.values and residuals
extract useful features of the value returned by forecast.nnetar.
An object of class "forecast" is a list containing at least the
following elements:
A list containing information about the fitted model
The name of the forecasting method as a character string
Point forecasts as a time series
Lower limits for prediction intervals
Upper limits for prediction intervals
The confidence values associated with the prediction intervals
The original time series (either object itself or the time series
used to create the model stored as object).
The external regressors used in fitting (if given).
Residuals from the fitted model. That is x minus fitted values.
Fitted values (one-step forecasts)
Other arguments
An object of class "modelAR" resulting from a call to
modelAR.
Number of periods for forecasting. If xreg is used, h
is ignored and the number of forecast periods is set to the number of rows
of xreg.
If TRUE, prediction intervals are produced, otherwise only point
forecasts are calculated. If PI is FALSE, then level,
fan, bootstrap and npaths are all ignored.
Confidence level for prediction intervals.
If TRUE, level is set to seq(51,99,by=3). This is
suitable for fan plots.
Future values of external regressor variables.
Box-Cox transformation parameter. If lambda="auto",
then a transformation is automatically selected using BoxCox.lambda.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.
If TRUE, then prediction intervals computed using
simulations with resampled residuals rather than normally distributed
errors. Ignored if innov is not NULL.
Number of sample paths used in computing simulated prediction intervals.
Values to use as innovations for prediction intervals. Must be
a matrix with h rows and npaths columns (vectors are coerced
into a matrix). If present, bootstrap is ignored.
Additional arguments passed to simulate.nnetar
Rob J Hyndman and Gabriel Caceres
Prediction intervals are calculated through simulations and can be slow. Note that if the model is too complex and overfits the data, the residuals can be arbitrarily small; if used for prediction interval calculations, they could lead to misleadingly small values.
nnetar.