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predict.Arima question
2 messages · Felipe Santos, Rob Hyndman
Unfortunately you can't do this using the arima() or predict() functions in the stats package. However, you can do what you want using the arima() function in the forecast package (available on CRAN). There is also a function best.arima() which selects the best model according to AIC, BIC or AICc. Best wishes, Rob
Felipe Santos wrote:
Hi,
I am trying to forecast a model using predict.Arima
I found arima model for a data set: x={x1,x2,x3,...,x(t)}
arima_model = arima(x,order=c(1,0,1))
I am forecasting the next N lags using predict:
arima_pred = predict(arima_model,n.ahead = N, se.fit=T)
If I have one more point in my series, let's say x(t+1). I do not want to
recalibrate themodel, I just want to forecast the next N-1 lags using the
same model for x={x1,x2,...x(t)} but without recalibrate arima.
How to do it using arima + predict.Arima ?
My problem is that I am trying to fit arima models by brute force ( trying
lots of combinations for p and q and chosing the best model by AIC and BIC )
I have a big time series and I am running calibration for some sub-sequence
and I trying to forecast some points. I repeat this process for the next
contiguous subsequence and try to forecast again, until the big series end.
Thanks
Felipe
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__________________________________________________ Professor Rob J Hyndman Department of Econometrics & Business Statistics, Monash University, VIC 3800, Australia http://www.robhyndman.info/