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.

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