Returns forecasts and other information for univariate Holt-Winters time series models.
# S3 method for HoltWinters
forecast(
object,
h = if (frequency(object$x) > 1) 2 * frequency(object$x) else 10,
level = c(80, 95),
fan = FALSE,
lambda = NULL,
biasadj = FALSE,
...
)An object of class forecast.
An object of class HoltWinters. Usually the result of
a call to stats::HoltWinters().
Number of periods for forecasting. Default value is twice the largest seasonal period (for seasonal data) or ten (for non-seasonal data).
Confidence levels for prediction intervals.
If TRUE, level is set to seq(51, 99, by = 3).
This is suitable for fan plots.
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.
Use adjusted back-transformed mean for Box-Cox
transformations. If transformed data is used to produce forecasts and fitted
values, a regular back transformation will result in median forecasts. If
biasadj is TRUE, an adjustment will be made to produce mean forecasts
and fitted values.
Other arguments are ignored.
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
This function calls stats::predict.HoltWinters() and constructs
an object of class forecast from the results.
It is included for completeness, but the ets() is recommended
for use instead of stats::HoltWinters.
fit <- HoltWinters(WWWusage, gamma = FALSE)
plot(forecast(fit))
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