[R] Time Series Issues, Stationarity ..
John C Frain
frainj at gmail.com
Mon Nov 26 22:32:10 CET 2007
With 24 values you are asking the impossible from your data. If you
use the standard Box Jenkins approach rather than an automatic ARIMA
and using any prior knowledge of the data you might manage some form
of forecast. Look at graphs of the data and their first differences.
Look at graphs of the autocorrelation and partial autocorrelation
functions. There are a range of text books that describe this kind of
manual Box-Jenkins approach.
There is an excellent account of stationarity and related matters in
Pfaff, B (2006), "Analysis of Integrated and Cointegrated Time Series
with R", in the Springer USE R! series. This also contains an account
of ARMA models.
Best regards
John
On 26/11/2007, Ozcan Asilkan <oasilkan at gmail.com> wrote:
> Hello,
>
> I am very new to R and Time Series. I need some help including R codes
> about the following issues. I' ll really appreciate any number of
> answers...
>
> # I have a time series data composed of 24 values:
> myinput = c(n1,n2...,n24);
> # In order to make a forecasting a, I use the following codes
> result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q)))
> result2 = forecast(result1,12)
> plot(result2)
>
> Now, by using R code...
>
> 1) How can I determine if my data is statitonary or not ? (trend &
> seasonal effects)
> 2) If not, how can I make it stationary ?
> 3) Is arima() function used only on STATIONARY data ? Or does it first
> determine if the data is stationary or not and makes it stationary ?
> (if it is non-stationary)
> 4) I tried different parameter values in arima() function, but every
> parameter gave very different results :(( . I
> even found & tried best.arima() function but it also gave
> unsatisfactory result. So, how can I calculate the optimum arima()
> parameters (p,d,q,P,D,Q) that fit my data best ?
>
> Thanks in advance, best wishes..
>
> Ozzy
>
> ______________________________________________
> R-help at 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.
>
--
John C Frain
Trinity College Dublin
Dublin 2
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj at tcd.ie
mailto:frainj at gmail.com
More information about the R-help
mailing list