This fits an exponential smoothing state space model with
model = 'ANN'
to y
, having first performed classic multiplicative
seasonal adjustment. A drift value is also calculated by
lsfit(0:(length(y) - 1), y)$coef[2] / 2
. In combination with forecast.thetam()
,
this provides identical results to forecast::thetaf(...)
. The purpose of splitting
it into a `model` and `forecast` functions is to make the approach consistent with other
modeling / forecasting approaches used in hybridModel()
.