# forecast.baggedETS

##### Forecasting using the bagged ETS method

Returns forecasts and other information for bagged ETS models.

- Keywords
- ts

##### Usage

```
# S3 method for baggedETS
forecast(object, h = ifelse(frequency(object$x) > 1, 2 *
frequency(object$x), 10), ...)
```

##### Arguments

##### Details

Intervals are calculated as min and max values over the point forecasts from the ETS models in the ensemble. I.e., the intervals are not prediction intervals, but give an indication of how different the forecasts within the ensemble are.

##### 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.

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`

).

The external regressors used in fitting (if given).

Residuals from the fitted model. That is x minus fitted values.

Fitted values (one-step forecasts)

Other arguments

##### References

Bergmeir, C., R. J. Hyndman, and J. M. Benitez (2016). Bagging Exponential Smoothing Methods using STL Decomposition and Box-Cox Transformation. International Journal of Forecasting 32, 303-312.

##### See Also

##### Examples

```
# NOT RUN {
fit <- baggedETS(WWWusage)
fcast <- forecast(fit)
plot(fcast)
# }
```

*Documentation reproduced from package forecast, version 8.1, License: GPL (>= 3)*