The bagged model forecasting method.

baggedModel(y, bootstrapped_series = bld.mbb.bootstrap(y, 100),
fn = c("ets", "auto.arima"), ...)
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.

modelsA list containing the fitted ensemble models.

methodThe name of the forecasting method as a character string

yThe original time series.

bootstrapped_seriesThe bootstrapped series.

modelargsThe arguments passed through to `fn`

.

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

residualsOriginal 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.

## 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