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

-- 
A. Phani Kishan
3rd Year B.Tech
Dept. of Computer Science & Engineering
IIT MADRAS
Ph: +919962363545

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