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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