The bagged model forecasting method.

## 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.

## Value

Returns an object of class "`baggedModel`

".

The function `print`

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

- models
A list containing the fitted ensemble models.

- method
The function for producing a forecastable model.

- y
The original time series.

- bootstrapped_series
The bootstrapped series.

- modelargs
The arguments passed through to

`fn`

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

- residuals
Original values minus fitted values.

## 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.