ARIMA.BIC: Example function: Calculating BIC for AR(1) model

In changepointGA: Changepoint Detection via Modified Genetic Algorithm

Example function: Calculating BIC for AR(1) model

Description

The objective function for changepoint search in Autoregressive moving average with model order selection via Bayesian Information Criterion (BIC).

Usage

ARIMA.BIC(chromosome, plen = 0, XMat, Xt)

Arguments

chromosome	The chromosome consists of the number of changepoints, the order of AR part, the order of MA part, the changepoint locations, and a value of time series length plus 1 (N+1) indicating the end of the chromosome.
plen	The number of model order parameters that need to be selected. If model order selection needs to be performed simultaneously with the changepoint detection task, plen should be nonzero.
XMat	A matrix contains the covariates, but not includes changepoint effects, for time series regression.
Xt	The simulated ARMA time series from ts.sim function.

Value

The BIC value of the objective function.

Examples

Ts = 1000
betaT = c(0.5) # intercept
XMatT = matrix(1, nrow=Ts, ncol=1)
colnames(XMatT) = "intercept"
sigmaT = 1
phiT = c(0.5)
thetaT = NULL
DeltaT = c(2, -2)
Cp.prop = c(1/4, 3/4)
CpLocT = floor(Ts*Cp.prop)

myts = ts.sim(beta=betaT, XMat=XMatT, sigma=sigmaT, phi=phiT, theta=thetaT,
              Delta=DeltaT, CpLoc=CpLocT, seed=1234)

# candidate changepoint configuration
chromosome = c(2, 250, 750, 1001)
ARIMA.BIC(chromosome, XMat=XMatT, Xt=myts)

changepointGA documentation built on June 8, 2025, 1:12 p.m.