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 probab
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 rece
What exactly do you mean by "apply" it to a different data set?
Unlike regular regressions, time series models don't (generally) use
new data to make forecasts ...
By the way, this is a good guide to the time series functionality
available in R: http://cran.r-project.org/web/views/TimeSeries.html
Colleagues
I am a new to R but already love it.
I have the following problem:
I fitted arima model to my time series like this (please ignore modeling
parameters as they are not important now):
x = scan("C:/data.txt")
x = ts(x, start=1, frequency=1)
x.fit<-arima(x, order = c(1,0,0), seasonal = l
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