Forecasts with ARIMA Models
In: Freakonometrics, 2016
academicJournal
Zugriff:
In our time series class this morning, I was discussing forecasts with ARIMA Models. Consider some simple stationnary AR(1) simulated time series n=95 set.seed(1) E=rnorm(n) X=rep(0,n) phi=.85 for(t in 2:n) X[t]=phi*X[t-1]+E[t] plot(X,type="l") If we fit an AR(1) model, model=arima(X,order=c(1,0,0), + include.mean = FALSE) P=predict(model,n.ahead=20) plot(P$pred) lines(P$pred+2*P$se,col="red") lines(P$pred-2*P$se,col="red") abline(h=0,lty=2) abline(h=2*P$se[20],lty=.
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Forecasts with ARIMA Models
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Autor/in / Beteiligte Person: | Charpentier, Arthur |
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Zeitschrift: | Freakonometrics, 2016 |
Veröffentlichung: | Freakonometrics, 2016 |
Medientyp: | academicJournal |
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