pairingits {iAR} | R Documentation |
Pairing two irregularly observed time series
Description
This method pairs the observational times of two irregularly observed time series.
Usage
pairingits(x, ...)
Arguments
x |
An object of class 'utilities'. |
... |
Additional arguments for pairing time series:
|
Details
The method checks the observational times in both input time series and pairs the measurements if they fall within the specified tolerance ('tol'). If a measurement in one series cannot be paired, it is filled with 'NA' values for the corresponding columns of the other series.
Value
An object of class 'utilities' with two slots:
series |
A matrix containing the paired time series, where unmatched measurements are filled with 'NA'. |
series_esd |
A matrix containing the paired error standard deviations of the time series, where unmatched measurements are filled with 'NA'. |
times |
A numeric vector with the paired observational times. |
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.
Examples
data(cvnovag)
data(cvnovar)
datag=cvnovag
datar=cvnovar
o1=iAR::utilities()
o1<-pairingits(o1, datag,datar,tol=0.1)
pargr1=na.omit(o1@paired)
st=apply(pargr1[,c(1,4)],1,mean)
model_BiAR <- BiAR(times = st,series=pargr1[,c(2,5)],series_esd=pargr1[,c(3,6)])
model_BiAR <- kalman(model_BiAR)