forecast.HoltWinters | R Documentation |
Returns forecasts and other information for univariate Holt-Winters time series models.
## S3 method for class 'HoltWinters' forecast( object, h = ifelse(frequency(object$x) > 1, 2 * frequency(object$x), 10), level = c(80, 95), fan = FALSE, lambda = NULL, biasadj = NULL, ... )
object |
An object of class " |
h |
Number of periods for forecasting |
level |
Confidence level for prediction intervals. |
fan |
If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots. |
lambda |
Box-Cox transformation parameter. If |
biasadj |
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. |
This function calls 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 HoltWinters
.
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.HoltWinters
.
An object of class "forecast"
is a list containing at least the
following elements:
model |
A list containing information about the fitted model |
method |
The name of the forecasting method as a character string |
mean |
Point forecasts as a time series |
lower |
Lower limits for prediction intervals |
upper |
Upper limits for prediction intervals |
level |
The confidence values associated with the prediction intervals |
x |
The original time series
(either |
residuals |
Residuals from the fitted model. |
fitted |
Fitted values (one-step forecasts) |
Rob J Hyndman
predict.HoltWinters
,
HoltWinters
.
fit <- HoltWinters(WWWusage,gamma=FALSE) plot(forecast(fit))
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