Returns forecasts and other information for univariate ETS models.
# S3 method for ets forecast(object, h = ifelse(object$m > 1, 2 * object$m, 10), level = c(80, 95), fan = FALSE, simulate = FALSE, bootstrap = FALSE, npaths = 5000, PI = TRUE, lambda = object$lambda, biasadj = NULL, ...)
An object of class "
ets". Usually the result of a call
Number of periods for forecasting
Confidence level for prediction intervals.
If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
If TRUE, prediction intervals are produced by simulation rather than using analytic formulae. Errors are assumed to be normally distributed.
If TRUE, then prediction intervals are produced by simulation using resampled errors (rather than normally distributed errors).
Number of sample paths used in computing simulated prediction intervals.
If TRUE, prediction intervals are produced, otherwise only point
forecasts are calculated. If
PI is FALSE, then
npaths are all
Box-Cox transformation parameter. If
then a transformation is automatically selected using
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.
An object of class "
summary is used to obtain and print a summary of the
results, while the function
plot produces a plot of the forecasts and
The generic accessor functions
extract useful features of the value returned by
An object of class
"forecast" is a list containing at least the
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
object itself or the time series used to create the model
Residuals from the fitted model. For models with additive errors, the residuals are x - fitted values. For models with multiplicative errors, the residuals are equal to x /(fitted values) - 1.
Fitted values (one-step forecasts)