changepointGA

Authors: Mo Li (mo.li@louisiana.edu), QiQi Lu (qlu2@vcu.edu)

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Overview

Detecting changepoints in a time series of length $N$ entails evaluating up to $2^{N-1}$ possible changepoint models, making exhaustive enumeration computationally infeasible. Genetic algorithms (GAs) provide a stochastic way to identify the structural changes: a population of candidate models evolves via selection, crossover, and mutation operators until it converges on one changepoint model that balances the goodness-of-fit with parsimony.

The R package changepointGA represents each candidate model using an integer-valued chromosome and provides three GA-based search strategies:

• cptga(): a basic single-population genetic algorithm;
• cptgaisl(): an island model genetic algorithm (IMGA);
• cptgascisl(): a structured and consensus-guided island model genetic algorithm (SC-IMGA).

The SC-IMGA extends the original island model algorithm through a structured birth-death-relocate mutation operator, cross-island consensus guidance, consensus-guided mutation candidate selection, and objective-based local refinement. These components are designed to improve the search over variable-dimensional changepoint configurations while retaining the general objective-function framework of changepointGA.

Parallel computing is supported on multi-core hardware. Users may supply custom objective functions or genetic operators, and the package supports both changepoint detection alone and simultaneous changepoint detection and integer-valued model-order selection.

The original cptga() and cptgaisl() algorithms have been evaluated through extensive simulations and applications to array-based comparative genomic hybridization data and a century-long temperature series. The SC-IMGA provides an additional search strategy for more challenging multiple-changepoint optimization problems.

Installation

You can install the version of changepointGA from CRAN:

install.packages("changepointGA")

or the development version from GitHub:

# install.packages("remotes")
remotes::install_github("mli171/changepointGA", build_vignettes = FALSE, force = TRUE)

Changepoint Detection Only

Call the library.

library(changepointGA)

An example of using cptga()

##### Stationary time series with autocorrelation
Ts = 1000
betaT = c(0.5, -0.5, 0.3) # intercept, B, D
period = 30
XMatT = cbind(rep(1, Ts), cos(2*pi*(1:Ts)/period), sin(2*pi*(1:Ts)/period))
colnames(XMatT) = c("intercept", "Bvalue", "DValue")
sigmaT = 1
phiT = c(0.5)
DeltaT = c(2, -2)
Cp.prop = c(1/4, 3/4)
CpLocT = floor(Ts*Cp.prop)

Xt = ts_sim(Ts=Ts, beta=betaT, XMat=XMatT, sigma=sigmaT, phi=phiT, theta=NULL, 
            Delta=DeltaT, CpLoc=CpLocT, seed=1234)

tim1 = Sys.time()
tmp1 = cptga(ObjFunc=arima_bic, N=Ts, XMat=XMatT, Xt=Xt)
tim2 = Sys.time()
summary(tmp1)
plot(tmp1, data=Xt)


tim2 - tim1

An example of using cptgaisl()

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

Xt = ts_sim(Ts=Ts, beta=betaT, XMat=XMatT, sigma=sigmaT, phi=phiT, theta=NULL, 
            Delta=DeltaT, CpLoc=CpLocT, seed=1234)


## No parallel computing
tim3 = Sys.time()
tmp2 = cptgaisl(ObjFunc=arima_bic, N=Ts, XMat=XMatT, Xt=Xt)
tim4 = Sys.time()
summary(tmp2)
plot(tmp2, data=Xt)


## Parallel computing
tim5 = Sys.time()
tmp3 = cptgaisl(ObjFunc=arima_bic, N=Ts, parallel=TRUE, nCore=5, XMat=XMatT, Xt=Xt)
tim6 = Sys.time()
summary(tmp3)
plot(tmp3, data=Xt)

tim4 - tim3
tim6 - tim5

An example of using cptgascisl()

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

Xt = ts_sim(Ts=Ts, beta=betaT, XMat=XMatT, sigma=sigmaT, phi=phiT, theta=NULL,
            Delta=DeltaT, CpLoc=CpLocT, seed=1234)


## Structured and consensus-guided island model GA
tim7 = Sys.time()
tmp4 = cptgascisl(ObjFunc=arima_bic, N=Ts, XMat=XMatT, Xt=Xt)
tim8 = Sys.time()

summary(tmp4)
plot(tmp4, data=Xt)

tim8 - tim7

Changepoint Detection + Model order selection

An example of using cptga()

Ts = 1000
betaT = c(0.5, -0.5, 0.3) # intercept, B, D
period = 30
XMatT = cbind(rep(1, Ts), cos(2*pi*(1:Ts)/period), sin(2*pi*(1:Ts)/period))
colnames(XMatT) = c("intercept", "Bvalue", "DValue")
sigmaT = 1
phiT = c(0.5, -0.5)
thetaT = c(0.8)
DeltaT = c(2, -2)
Cp.prop = c(1/4, 3/4)
CpLocT = floor(Ts*Cp.prop)

Xt = ts_sim(Ts=Ts, beta=betaT, XMat=XMatT, sigma=sigmaT, phi=phiT, theta=thetaT, 
            Delta=DeltaT, CpLoc=CpLocT, seed=1234)

prange = list(ar=c(0,2), ma=c(0,2))

tim1 = Sys.time()
tmp1 = cptga(ObjFunc=arima_bic_order_pq, N=Ts, prange=prange, option="both", 
             XMat=XMatT, Xt=Xt)
tim2 = Sys.time()
summary(tmp1)
plot(tmp1, data=Xt)

tim2 - tim1

An example of using cptgaisl()

Ts = 1000
betaT = c(0.5, -0.5, 0.3) # intercept, B, D
period = 30
XMatT = cbind(rep(1, Ts), cos(2*pi*(1:Ts)/period), sin(2*pi*(1:Ts)/period))
colnames(XMatT) = c("intercept", "Bvalue", "DValue")
sigmaT = 1
phiT = c(0.5, -0.5)
thetaT = c(0.8)
DeltaT = c(2, -2)
Cp.prop = c(1/4, 3/4)
CpLocT = floor(Ts*Cp.prop)

Xt = ts_sim(Ts=Ts, beta=betaT, XMat=XMatT, sigma=sigmaT, phi=phiT, theta=thetaT, 
            Delta=DeltaT, CpLoc=CpLocT, seed=1234)

prange = list(ar=c(0,2), ma=c(0,2))

tim3 = Sys.time()
tmp2 = cptgaisl(ObjFunc=arima_bic_order_pq, N=Ts, prange=prange, option="both", 
                XMat=XMatT, Xt=Xt)
tim4 = Sys.time()
summary(tmp2)
plot(tmp2, data=Xt)


tim5 = Sys.time()
tmp3 = cptgaisl(ObjFunc=arima_bic_order_pq, N=Ts, prange=prange, option="both", 
                parallel=TRUE, nCore=5, XMat=XMatT, Xt=Xt)
tim6 = Sys.time()
summary(tmp3)
plot(tmp3, data=Xt)

tim4 - tim3
tim6 - tim5

An example of using cptgascisl()

Ts = 1000
betaT = c(0.5, -0.5, 0.3) # intercept, B, D
period = 30
XMatT = cbind(rep(1, Ts), cos(2*pi*(1:Ts)/period), sin(2*pi*(1:Ts)/period))
colnames(XMatT) = c("intercept", "Bvalue", "DValue")
sigmaT = 1
phiT = c(0.5, -0.5)
thetaT = c(0.8)
DeltaT = c(2, -2)
Cp.prop = c(1/4, 3/4)
CpLocT = floor(Ts*Cp.prop)

Xt = ts_sim(Ts=Ts, beta=betaT, XMat=XMatT, sigma=sigmaT, phi=phiT, theta=thetaT,
            Delta=DeltaT, CpLoc=CpLocT, seed=1234)

prange = list(ar=c(0,2), ma=c(0,2))

tim7 = Sys.time()
tmp4 = cptgascisl(ObjFunc=arima_bic_order_pq, N=Ts, prange=prange,
                  option="both", XMat=XMatT, Xt=Xt)
tim8 = Sys.time()

summary(tmp4)
plot(tmp4, data=Xt)

tim8 - tim7

Code style

Before pushing changes, please run

styler::style_pkg()

to ensure your code follows the tidyverse style guide.

Citation

If you use changepointGA in your research, please cite the article corresponding to the method used:

• For cptga() and cptgaisl(), please cite:

  Li, M., & Lu, Q. (2026). changepointGA: An R package for Fast Changepoint Detection via Genetic Algorithm. The R Journal.

• For cptgascisl(), please cite:

  Li, M. (2026). Structured and Consensus-Guided Island Model Genetic Algorithm for Multiple Changepoint Detection. Manuscript in preparation for publication.