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.
