BIARforecast {iAR}

Forecast from BIAR model

Description

Forecast from models fitted by BIARkalman

Usage

BIARforecast(phiR, phiI, y1, y2, t, tAhead)

Arguments

phiR

Autocorrelation coefficient of BIAR model.

phiI

Cross-correlation coefficient of BIAR model.

y1

Array with the observations of the first time series of the BIAR process.

y2

Array with the observations of the second time series of the BIAR process.

t

Array with the observational times.

tAhead

The time ahead for which the forecast is required.

Value

A list with the following components:

• fitted Fitted values by the BIAR model.

• forecast Point forecast in the time ahead required.

• Lambda Lambda value estimated by the BIAR model at the last time point.

• Sighat Covariance matrix estimated by the BIAR model at the last time point.

References

Elorrieta F, Eyheramendy S, Palma W, Ojeda C (2021). “A novel bivariate autoregressive model for predicting and forecasting irregularly observed time series.” Monthly Notices of the Royal Astronomical Society, 505(1), 1105–1116. ISSN 0035-8711, doi: 10.1093/mnras/stab1216, https://academic.oup.com/mnras/article-pdf/505/1/1105/38391762/stab1216.pdf.

See Also

BIARsample, BIARkalman, BIARfit

Examples

#Simulated Data
n=100
set.seed(6714)
st<-gentime(n)
x=BIARsample(n=n,phiR=0.9,phiI=0.3,st=st)
biar=iAR::BIARkalman(y1=x$y[1,],y2=x$y[2,],t=st)
forBIAR<-BIARforecast(phiR=biar$phiR,phiI=biar$phiI,y1=x$y[1,],y2=x$y[2,],t=st,tAhead=c(1.3))