R: Forecasting using a bagged model

baggedModel {forecast} | R Documentation

## Forecasting using a bagged model

### Description

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.

### 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.

`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.

### 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 <- baggedModel(WWWusage)
    fcast <- forecast(fit)
    plot(fcast)

[Package forecast version 8.12 Index]