# baggedModel

##### Forecasting using a bagged model

The bagged model forecasting method.

- Keywords
- ts

##### Usage

```
baggedModel(y, bootstrapped_series = bld.mbb.bootstrap(y, 100),
fn = ets, ...)
```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.

- fn
the forecast function to use. Default is

`ets`

.- …
Other arguments passed to the forecast function.

##### Details

This function implements the bagged model forecasting method described in
Bergmeir et al. By default, the `ets`

function is applied to all
bootstrapped series. Base models other than `ets`

can be given by the
parameter `fn`

. 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.baggedModel`

can then be used to calculate forecasts.

`baggedETS`

is a wrapper for `baggedModel`

, setting `fn`

to "ets".
This function is included for backwards compatibility only, and may be
deprecated in the future.

##### Value

Returns an object of class "`baggedModel`

".

The function `print`

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

A list containing the fitted ensemble models.

The function for producing a forecastable model.

The original time series.

The bootstrapped series.

The arguments passed through to `fn`

.

Fitted values (one-step forecasts). The mean 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 <- baggedModel(WWWusage)
fcast <- forecast(fit)
plot(fcast)
# }
```

*Documentation reproduced from package forecast, version 8.5, License: GPL-3*