ARIMA.BIC.Order: Calculating BIC for Multiple changepoint detection with model...

In changepointGA: Changepoint Detection via Modified Genetic Algorithm

Calculating BIC for Multiple changepoint detection with model order selection

Description

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

Usage

ARIMA.BIC.Order(chromosome, plen = 2, XMat, Xt)

Arguments

chromosome	A vector consists of the number of changepoints, the order of AR component (refers to the number of lagged terms used to model the current value of a time series), the order of MA component (refers to the number of lagged error terms used to model the current value of a time series), the changepoint locations, and a value of time series sample size 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

N <- 1000
XMatT <- matrix(1, nrow = N, ncol = 1)
Xt <- ts.sim(
  beta = 0.5, XMat = XMatT, sigma = 1, phi = 0.5, theta = 0.8,
  Delta = c(2, -2), CpLoc = c(250, 750), seed = 1234
)

# one chromosome representation
chromosome <- c(2, 1, 1, 250, 750, 1001)
ARIMA.BIC.Order(chromosome, plen = 2, XMat = XMatT, Xt = Xt)

changepointGA documentation built on Nov. 5, 2025, 6:54 p.m.