Description Usage Arguments Details Value Author(s) References Examples
The bagged ETS forecasting method.
1 | baggedETS(y, bootstrapped_series=bld.mbb.bootstrap(y, 100), ...)
|
y |
A numeric vector or time series. |
bootstrapped_series |
bootstrapped versions of y. |
... |
Other arguments passed to |
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.
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 |
fitted |
Fitted values (one-step forecasts). The mean is 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.
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