Forecast_iARModels {iAR}

Forecast from iAR package model's

Description

Forecast with any of the models available in the iAR package

Usage

Forecast_iARModels(
  phi,
  y,
  st,
  tAhead,
  model = "iAR",
  mu = NULL,
  phiI = NULL,
  nu = NULL,
  level = 95
)

Arguments

phi

Autocorrelation coefficient estimated by the method specified.

y

Array with the time series observations.

st

Array with the observational times.

tAhead

The time ahead for which the forecast is required.

model

model to be used for the forecast. The default is to use the iAR model. Other models available are "iAR-T", "iAR-Gamma", "CiAR" and "BiAR".

mu

Level parameter of the IAR-Gamma process. A positive value.

phiI

Imaginary parameter of CIAR model or Cross-correlation parameter of BIAR model.

nu

degrees of freedom parameter of iAR-T model.

level

significance level for the confidence interval. The default value is 95.

Value

A dataframe with the following columns:

• tAhead The time ahead used for the forecast.

• forecast Point forecast in the time ahead required.

• stderror Standard error of the forecast.

• lowerCI Lower limit of the confidence interval.

• upperCI Upper limit of the confidence interval.

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, IARforecast, IARgforecast, IARforecast, BIARforecast

Examples

st <- gentime(n=200,lambda1=15,lambda2=2)
y  <- IARsample(phi=0.9,n=200,st=st)
model<-IARloglik(y=y$series,st=st)
phi=model$phi
forIAR<-IARforecast(phi=phi,y$series,st=st,tAhead=c(1.3),standardized=FALSE,zero.mean=FALSE)
forIAR
forIAR<-Forecast_iARModels(phi=phi,y=y$series,st=st,tAhead=c(1.3,2.6))
forIAR