Hi Phani,
to get the best Holt's model, I would simply wrap a suitable function
calling ets() within optim() and optimize for alpha and beta - the
values given by ets() without constraints would probably be good
starting values, but you had better start the optimization with a
variety of starting values to make sure you don't end up in a local minimum.
I know of no comparison just between Holt and Brown - but you could use
the above methods and the M3 Competition dataset (in Mcomp) to look how
the two methods compare on a (more or less) benchmark dataset.
HTH
Stephan
phani kishan schrieb:
Hey,
I am using the ets() function in the forecast package to find out the best
fit parameters for my time-series. I have about 50 sets of time series data.
I'm currently using the function as follows:
ets(x,model="AZZ",opt.crit="mse")
As to my observation about 5-10 of them have been identified by ets to have
a trend and an alpha, beta values have been thrown up - which have been same
in all these cases. When I read up online it came up as a Brown's double
exponential smoothing as opposed to Holt's exponential smoothing (where
alpha and beta differ). I am guessing this is happening as AIC/AICc/BIC
select a model based on accuracy as well as a weight on number of parameters
(1 in case of brown's, 2 in case of holt's). Now if I want to see results of
the best parameters from the Holt's method, how should I go about it?
And is there any study comparing the accuracy of brown's double exponential
model versus holt's exponential model?
Thanks in advance,
Phani
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