The bagged ETS forecasting method.
baggedETS(y, bootstrapped_series = bld.mbb.bootstrap(y, 100), ...)
A numeric vector or time series of class ts
.
bootstrapped versions of y.
Other arguments passed to ets
.
Returns an object of class "baggedETS
".
The function print
is used to obtain and print a summary of the
results.
A list containing the fitted ETS ensemble models.
The name of the forecasting method as a character string
The original time series.
The bootstrapped series.
The arguments passed through to
ets
.
Fitted values (one-step forecasts). The mean is of the fitted values is calculated over the ensemble.
Original values minus fitted values.
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.
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.
# NOT RUN {
fit <- baggedETS(WWWusage)
fcast <- forecast(fit)
plot(fcast)
# }
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