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
to ets.
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 level,
fan, simulate, bootstrap and npaths are all
ignored.
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.
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.ets.
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).
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)
# NOT RUN {
fit <- ets(USAccDeaths)
plot(forecast(fit,h=48))
# }
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