baggedModel | R Documentation |

The bagged model forecasting method.

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

`y` |
A numeric vector or time series of class |

`bootstrapped_series` |
bootstrapped versions of y. |

`fn` |
the forecast function to use. Default is |

`...` |
Other arguments passed to the forecast function. |

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.

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 |

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

`residuals` |
Original values minus fitted values. |

Christoph Bergmeir, Fotios Petropoulos

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.

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

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.