Fitting a multivariate ARIMA model with R I have 4 correlated time series, and I want to predict one of them, from the other 3. There is a clear seasonal effect in the 4 time series, so my first thought was to fit a multivariate ARIMA model, but I can not seem to find an R-function for this.
 A: As @IrishStat says, you want a transfer function or ARMAX model. This can be fitted using the TSA package in R.
A: It appears that you want to model 1 of them given the other 3. This is called a Transfer Function and also sometimes an ARMAX model. You will be interested in capturing not only contemporaneous effects but lag effects.The unexplained component ( the current error term) might be further partitioned into some autoregressive structure (ARIMA) and/or sOme deterministic structure (  Pulses, Level Shifts , Seasonal Pulses, Local Time Trends. I am not an R expert but I don't believe that functionality currently exists. You might want to use the internet http://www.google.com/search?sourceid=navclient&ie=UTF-8&rlz=1T4SUNA_enUS287US288&q=multivariate+box-jenkins and to search for "MULTIVARIATE BOX-JENKINS".
A: I am not sure if you are still looking for a solution or not but I think windowing in Rapidminer might work in here. you can give this a horizon value, using that value, you will build your model. for example you will take yesterday values of other 3 variables to predict today's fourth independent variable. 
