# baggedETS

##### Forecasting using the bagged ETS method

The bagged ETS forecasting method.

- Keywords
- ts

##### Usage

`baggedETS(y, bootstrapped_series = bld.mbb.bootstrap(y, 100), ...)`

##### Arguments

- y
A numeric vector or time series of class

`ts`

.- bootstrapped_series
bootstrapped versions of y.

- …
Other arguments passed to

`ets`

.

##### Details

This function implements the bagged ETS forecasting method described in
Bergmeir et al. The `ets`

function is applied to all
bootstrapped series. Using the default parameters, the function
`bld.mbb.bootstrap`

is used to calculate the bootstrapped series
with the Box-Cox and Loess-based decomposition (BLD) bootstrap. The function
`forecast.baggedETS`

can then be used to calculate forecasts.

##### Value

Returns an object of class "`baggedETS`

".

The function `print`

is used to obtain and print a summary of the
results.

A list containing the fitted ETS ensemble models.

The name of the forecasting method as a character string

The original time series.

The bootstrapped series.

The arguments passed through to
`ets`

.

Fitted values (one-step forecasts). The mean is of the fitted values is calculated over the ensemble.

Original values minus fitted values.

##### 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.

##### Examples

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

*Documentation reproduced from package forecast, version 8.2, License: GPL-3*