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,
...
)
```

object

An object of class "`ets`

". Usually the result of a call
to `ets`

.

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.

simulate

If TRUE, prediction intervals are produced by simulation rather than using analytic formulae. Errors are assumed to be normally distributed.

bootstrap

If TRUE, then prediction intervals are produced by simulation using resampled errors (rather than normally distributed errors).

npaths

Number of sample paths used in computing simulated prediction intervals.

PI

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.

lambda

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.

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.

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)) # }