[R] how to increase speed for function?/time efficiency of below function

[R. Michael Weylandt]

It's rather inappropriate to use someone else's code and not to give
them credit:

http://www.mirrorservice.org/sites/lib.stat.cmu.edu/general/tsa2/Rcode/itall.R

Michael

On Thu, Mar 29, 2012 at 7:54 AM, sagarnikam123 <sagarnikam123 at gmail.com> wrote:
> i am using sarima() function as below
> ___________________________________________________________________________________________
> sarima=function(data,p,d,q,P=0,D=0,Q=0,S=-1,tol=.001){
>  n=length(data)
>  constant=1:n
>  xmean=matrix(1,n,1)
>  if (d>0 & D>0)
>    fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q),
> period=S),
>            optim.control=list(trace=1,REPORT=1,reltol=tol))
>  if (d>0 & D==0)
>    fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q),
> period=S),
>            xreg=constant,include.mean=F,
> optim.control=list(trace=1,REPORT=1,reltol=tol))
>  if (d==0 & D==0)
>    fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q),
> period=S),
>            xreg=xmean,include.mean=F,
> optim.control=list(trace=1,REPORT=1,reltol=tol))
>  if (d==0 & D>0)
>    fitit=arima(data, order=c(p,d,q), seasonal=list(order=c(P,D,Q),
> period=S),
>            xreg=constant,include.mean=F,
> optim.control=list(trace=1,REPORT=1,reltol=tol))
>  if (S < 0) goof=20 else goof=3*S
>  tsdiag(fitit,gof.lag=goof)
>  k=length(fitit$coef)
>  BIC=log(fitit$sigma2)+(k*log(n)/n)
>  AICc=log(fitit$sigma2)+((n+k)/(n-k-2))
>  AIC=log(fitit$sigma2)+((n+2*k)/n)
>  innov<<-fitit$resid
>  list(fit=fitit, AIC=AIC, AICc=AICc, BIC=BIC)
> }
> --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
> it takes 1-2 minutes for one time series,i have 4500 times series data,how
> to increase speed/time of execution
> is there any concept of threading/parallel computing in R
> Please..Please.....can u suggest me code for above function,which will be
> time efficient,i am frustrated with time consumption & reading material on
> parallel computing, but i cant understand it properly
>
>
>
> --
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>
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