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 Michael On Tue, Apr 17, 2012 at 5:54 PM, sergey777 <ssam...@uwo.ca> wrote: > 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 = list(order=c(0,0,1))) > > Now I want to use this model for forecasting and backtesting (!). My goal is > to apply exactly this model to different data – another time series object, > let’s call it “y”. How can I do this in R. > > One of the options is to extract coefficients and to create my own function > that can be applied to any time series but I suspect and hope that there is > a better way of doing this. > If there is not an easy option can anyone suggest a complete equation that > includes seasonal terms that can be easily programmed (for example in C) > for a person who knows some programming but very little math. > > Thank you. > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Manually-reconstructing-arima-model-from-coefficients-tp4566082p4566082.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > 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. ______________________________________________ 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.
