# forecast.ets

From forecast v6.0
by Rob Hyndman

##### Forecasting using ETS models

Returns forecasts and other information for univariate ETS models.

- Keywords
- ts

##### Usage

```
## S3 method for class '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, ...)
```

##### Arguments

- 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(50,99,by=1). This is suitable for fan plots.
- simulate
- If TRUE, prediction intervals produced by simulation rather than using analytic formulae.
- bootstrap
- If TRUE, and if
`simulate=TRUE`

, then simulation uses 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. Ignored if NULL. Otherwise, forecasts back-transformed via an inverse Box-Cox transformation.
- ...
- Other arguments.

##### Value

- 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: 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 `object`

itself or the time series used to create the model stored as`object`

).residuals 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 Fitted values (one-step forecasts)

##### See Also

##### Examples

```
fit <- ets(USAccDeaths)
plot(forecast(fit,h=48))
```

*Documentation reproduced from package forecast, version 6.0, License: GPL (>= 2)*

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