Returns forecasts and other information for user-defined models.
# S3 method for modelAR
forecast(
object,
h = if (object$m > 1) 2 * object$m else 10,
PI = FALSE,
level = c(80, 95),
fan = FALSE,
xreg = NULL,
lambda = object$lambda,
bootstrap = FALSE,
innov = NULL,
npaths = 1000,
...
)An object of class forecast.
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 levels for prediction intervals.
If TRUE, level is set to seq(51, 99, by = 3).
This is suitable for fan plots.
Future values of any regression variables. A numerical vector or matrix of external regressors; it should not be a data frame.
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 are produced by
simulation using resampled errors (rather than normally distributed errors). Ignored if innov is not NULL.
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.
Number of sample paths used in computing simulated prediction intervals.
Additional arguments passed to simulate.nnetar().
An object of class forecast is a list usually 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.
Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.
Fitted values (one-step forecasts)
The function summary can be used to obtain and print a summary of the
results, while the functions plot and autoplot produce plots of the forecasts and
prediction intervals. The generic accessors functions fitted.values and residuals
extract various useful features from the underlying model.
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().