The type of ETS model to fit on the decomposed trend. Only applicable to
"msdecompose" class. This is then returned in parameter "esmodel". If NULL, then
it will be selected automatically based on the type of the used decomposition (either
among pure additive or among pure additive ETS models).
Value
Returns object of class "smooth.forecast", which contains:
model - the estimated model (ES / CES / GUM / SSARIMA).
method - the name of the estimated model (ES / CES / GUM / SSARIMA).
forecast aka mean - point forecasts of the model
(conditional mean).
lower - lower bound of prediction interval.
upper - upper bound of prediction interval.
level - confidence level.
interval - binary variable (whether interval were produced or not).
Details
This is not a compulsory function. You can simply use es,
ces, gum or ssarima without
forecast.smooth. But if you are really used to forecast
function, then go ahead!
References
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008)
Forecasting with exponential smoothing: the state space approach,
Springer-Verlag. http://www.exponentialsmoothing.net.