baggedModel: Forecasting using a bagged model

In robjhyndman/forecast: Forecasting Functions for Time Series and Linear Models

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)

robjhyndman/forecast documentation built on March 14, 2024, 11:18 p.m.