[R] Exponential Smoothing: Forecast package

[Stephan Kolassa]

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
>