R: Forecasting using the bagged ETS method

baggedETS {forecast} | R Documentation

## Forecasting using the bagged ETS method

### Description

The bagged ETS forecasting method.

### Usage

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

### Arguments

`y` | A numeric vector or time series.
`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.

`models` | A list containing the fitted ETS ensemble models.
`method` | The name of the forecasting method as a character string
`y` | The original time series.
`bootstrapped_series` | The bootstrapped series.
`etsargs` | The arguments passed through to `ets`.
`fitted` | Fitted values (one-step forecasts). The mean is of the fitted values is calculated over the ensemble.
`residuals` | Original values minus fitted values.

### Author(s)

Christoph Bergmeir, Fotios Petropoulos

### 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

    fit <- baggedETS(WWWusage)
    fcast <- forecast(fit)
    plot(fcast)

[Package forecast version 8.0 Index]