Dear R-help-archive.. I am trying to figure out how to make arima prediction when I have a process involving multivariate time series input, and one output time series (output is to be predicted) .. (thus strictly speaking its an ARMAX process). I know that the arima function of R was not designed to handle multivariate analysis (there is dse but it doesnt handle arma multivariate analysis, only simulations). But there is this beautiful "xreg" as parameter for arima and I was wondering.. for the case of one output series I can actually "trick" R in doing multivariate time series for me no?.. because I saw in the documentation, xreg can be inputed as a ---matrix--- with output.len (length of output data) number of rows.. So in fact I can let the different columns of xreg to actually be the different input time series I need!
Is anyone familiar in how arima with xreg as given estimate models? .. how is the model assumed? supposing I write : arima(y, xreg=U, order=c(3,0,2)) how is y_t calculated? (supposing U has 2 columns, with U[1] being first column and U[2] second column) is it y_t = theta_(t-1)y_t-1 + .... + theta_t-3 y_t-3 + intercept + U[1]_t + psi[1]_t-1 U[1]_t-1 + psi[1]_t-2 U[1]_t-2 + ....+ psi[2]U[2]_t-2 + e_t + phi_t-1 e_t-1 + phi_t-2 e_t-2 ?? e_t .. etc. are the white noise series of the model. the documentation is totally vague when it comes to xreg. I hope it is like above :) Would appreciate any remarks or comments. Thanks in advance. Sincerely, Jose ______________________________________________ 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.
