If you just want point forecasts, it's simple:
Let your original series be X_t, t=1, ..., N.
Let Y_t = log(X_t).
Let Z_t = Y_t - Y_{t-1}, t = 2, ..., N.
Fit your model and forecast, obtaining Z-hat__1, ..., Z-hat_10.
Then Y-hat_{N+1} = Y_N + Z-hat_1, Y-hat_{N+2} = Y-hat_{N+1} + Z-hat_2,
....., Y-hat_{N+10} = Y-hat_{N+9} + Z-hat_10.
In R, let your forecast values be the vector "Zhat" (a vector of length 10).
Then do:
Yhat <- cumsum(c(Y[N],Zhat))[-1]
Xhat <- exp(Yhat)
Get error bounds on the forecasts is more problematic.
cheers,
Rolf Turner
On 02/05/2013 11:49 PM, Mahmoud Coker wrote:
Good morning to you all,
Sorry for taking your time from your research and
teaching schedules.
If you have a non-stationary univariate time Series
data that has the transformation:
Say; l.dat<-log (series)
d.ldat<-diff (l.dat, differences=1)
and you fit say arima model.
predit.arima<-predict (fit.series, n.ahead=10,
xregnew= (n+1) :( n+10))
How could I re-transform
"prediction$pred" to the level data since it has been differenced once? I know
exp (prediction$pred) will bring the inverse of the log
transform but what about the differenced transform? This is my question.
I would be very grateful if you could help me with
this.Thank you very much in anticipation.
______________________________________________
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.
