Bayesian Methods for Change Point Analysis

Performs change point detection on univariate and multivariate time series (Martínez & Mena, 2014, ; Corradin, Danese & Ongaro, 2022, ) and clusters time-dependent data with common change points (Corradin, Danese, KhudaBukhsh & Ongaro, 2026, ).

BayesChange provides C++ functions to perform Bayesian change points analysis.

Installation

To install BayesChange the package devtools is needed.

install.packages("devtools")

Now BayesChange can be installed through the GitHub repository of the package:

devtools::install_github("lucadanese/BayesChange")

Package contents

The package contains two main functions:

• detect_cp change points detection on time series and epidemic diffusions.
• clust_cp clustering of time series or epidemic diffusions with common change points.

Additional methods and functions are included:

• print() and summary() return information about the algorithm.
• posterior_estimate() estimates the change points or the final partition of the data.
• plot() provides a graphical representation of the results.
• plot_psm() provides the posterior similarity matrix for the output of clust_cp.
• sim_epi_data() generates an arbitrary number of simulated survival functions.

Detect change points

library(BayesChange)

## Univariate time series

data("stock_uni")

params_uni <- list(a = 1,
                   b = 1,
                   c = 1,
                   phi = 0.1)

out <- clust_cp(data = stock_uni[1:5,], n_iterations = 7500, n_burnin = 2500,
                L = 1, q = 0.5, B = 10000, params = params_uni, kernel = "ts")

print(out)
summary(out)
posterior_estimate(out)
plot(out)

## Multivariate time series

data("stock_multi")

params_multi <- list(m_0 = rep(0,2),
                     k_0 = 1,
                     nu_0 = 10,
                     S_0 = diag(1,2,2),
                     phi = 0.1)

out <- clust_cp(data = stock_multi[,,1:5], n_iterations = 7500, n_burnin = 2500,
                L = 1, B = 10000, params = params_multi, kernel = "ts")

print(out)
summary(out)
posterior_estimate(out)
plot(out)

## Epidemic diffusions

data("epi_synthetic_multi")

params_epi <- list(M = 250, xi = 1/8,
                   alpha_SM = 1,
                   a0 = 4,
                   b0 = 10,
                   I0_var = 0.1,
                   avg_blk = 2)

out <- clust_cp(epi_synthetic_multi, n_iterations = 5000, n_burnin = 2000,
                L = 1, B = 1000, params = params_epi, kernel = "epi")

print(out)
summary(out)
posterior_estimate(out)
plot(out)

Cluster time dependent data with common change points

## Univariate time series

data("stock_uni")

params_uni <- list(a = 1,
                   b = 1,
                   c = 1,
                   phi = 0.1)

out <- clust_cp(data = stock_uni[1:5,], n_iterations = 7500, n_burnin = 2500,
                L = 1, q = 0.5, B = 10000, params = params_uni, kernel = "ts")

print(out)
summary(out)
posterior_estimate(out)
plot(out)

## Multivariate time series

data("stock_multi")

params_multi <- list(m_0 = rep(0,2),
                     k_0 = 1,
                     nu_0 = 10,
                     S_0 = diag(1,2,2),
                     phi = 0.1)

out <- clust_cp(data = stock_multi[,,1:5], n_iterations = 7500, n_burnin = 2500,
                L = 1, B = 10000, params = params_multi, kernel = "ts")

print(out)
summary(out)
posterior_estimate(out)
plot(out)

## Epidemic diffusions

data("epi_synthetic_multi")

params_epi <- list(M = 250, xi = 1/8,
                   alpha_SM = 1,
                   a0 = 4,
                   b0 = 10,
                   I0_var = 0.1,
                   avg_blk = 2)

out <- clust_cp(epi_synthetic_multi, n_iterations = 5000, n_burnin = 2000,
                L = 1, B = 1000, params = params_epi, kernel = "epi")

print(out)
summary(out)
posterior_estimate(out)
plot(out)