IARgforecast {iAR}

Forecast from IAR-Gamma model

Description

Forecast from models fitted by IARgamma

Usage

IARgforecast(phi, mu, y, st, tAhead)

Arguments

phi

Estimated phi parameter by the iAR-Gamma model.

mu

Estimated mu parameter by the iAR-Gamma model.

y

Array with the time series observations.

st

Array with the irregular observational times.

tAhead

The time ahead for forecast is required.

Value

Forecasted value from the iAR-Gamma model

References

Eyheramendy S, Elorrieta F, Palma W (2018). “An irregular discrete time series model to identify residuals with autocorrelation in astronomical light curves.” Monthly Notices of the Royal Astronomical Society, 481(4), 4311–4322. ISSN 0035-8711, doi: 10.1093/mnras/sty2487, https://academic.oup.com/mnras/article-pdf/481/4/4311/25906473/sty2487.pdf.

See Also

gentime, IARgsample, IARgamma, IARgfit

Examples

n=100
set.seed(6714)
st<-gentime(n)
y<-IARgsample(phi=0.9,st=st,n=n,sigma2=1,mu=1)
y<-y$y
n=length(y)
p=trunc(n*0.99)
ytr=y[1:p]
yte=y[(p+1):n]
str=st[1:p]
ste=st[(p+1):n]
tahead=ste-str[p]
model<-IARgamma(ytr, st=str)
phi=model$phi
muest=model$mu
sigmaest=model$sigma
fit=IARgforecast(phi=phi,mu=muest,y=ytr,st=str,tAhead=tahead)