Ahh, I understand -- unfortunately, I'm not aware of an easy way to do this so you'll have to hack your own: this doesn't look too hard however, if you call
getAnywhere(predict.Arima) you can get the prediction scheme R uses. It seems that most of the heavy lifting is already in C so you'd probably be best served by simply updating the residuals series. Michael On Tue, Apr 17, 2012 at 9:21 PM, Sergey Samsonov <ssam...@uwo.ca> wrote: > Michael > > My final goal is to perform forecasting in real time. My historical data > that is used for training consist of about 2000 samples. Fitting ARIMA model > x.fit<-arima(x, order = c(5,0,0), seasonal = list(order=c(0,0,1))) takes > about 3-5 minutes, often I do not have so much time between receiving new > samples of data. Therefore, I want to re-create my arima model let's say > only every 50 samples but I want to update my forecast every time new data > sample arrives (in a real time). > > In other words I want to apply my arima model to forecasting future events > that will occurre not right after the model was created but some time later > after a few more intermediate samples were received. I think this problem is > similar to applying already fitted arima forecasting to a new time series > object that has similar statistical properties as a tested set, since these > are the same series just shifted in the future. ______________________________________________ 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.
