Hi Phani,
something like this looks promising:
#############################################
library(forecast)
library(Mcomp)
MAPE.for.Holt <- function (x,series,bignum=10e6) {
foo <-
try(ets(series$x,model="AAN",damped=FALSE,alpha=x[1],beta=x[2],restrict=FALSE),silent=TRUE)
if ( class(foo) == "try-error" ) {
bignum
} else {
mean(abs(fitted(foo)-series$x)/series$x,na.rm=TRUE)
}
}
bar <- optim(par=c(.1,.1),fn=MAPE.for.Holt,series=M3[[1]])
#############################################
At least it converges. However, I have had problems with parameters
leaving the allowed space (that's what the try() and the bignum is for),
and even with convergence, some unrealistically big smoothing constants
resulted, which in turn were not very stable for varying starting
parameters...
HTH,
Stephan
phani kishan schrieb:
Hey,
Thanks for the tip Stephan. But you could tell me how to pass the series to
the function calling ets?
Initially I planned to do it this way:
wrapper<-function(x)
{
alpha<-x[1]
beta<-x[2]
ph<-x[3]
series<-x[4]
foofit<-ets(series,model="AZZ",alpha=alpha,beta=beta,phi=phi,additive.only=T,opt.crit=c("mse"))
accuracy(foofit)[5] ##for MAPE
}
I then planned to use the optim using
optim(c(alpha,beta,phi,series),wrapper)
What I hoped to do is also select MAPE as a criteria for selection of my
alpha and beta.
However I shouldn't pass my series in this form right? As it would be
"optimized" too in the process? Could you suggest a way around this.
And I did find a way around could this allow me to set MAPE as a criteria?
Phani
On Tue, Jun 29, 2010 at 12:47 AM, Stephan Kolassa <stephan.kola...@gmx.de>wrote:
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
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
