Package 'hdbinseg'

Title: Change-Point Analysis of High-Dimensional Time Series via Binary Segmentation
Description: Binary segmentation methods for detecting and estimating multiple change-points in the mean or second-order structure of high-dimensional time series as described in Cho and Fryzlewicz (2014) <doi:10.1111/rssb.12079> and Cho (2016) <doi:10.1214/16-EJS1155>.
Authors: Haeran Cho [aut, cre], Piotr Fryzlewicz [aut]
Maintainer: Haeran Cho <[email protected]>
License: GPL (>= 3)
Version: 1.0.3
Built: 2026-06-06 06:34:03 UTC
Source: https://github.com/cran/hdbinseg

Help Index

Double CUSUM Binary Segmentation

Description

Perform the Double CUSUM Binary Segmentation algorithm detecting change points in the mean or second-order structure of the data.

Usage

dcbs.alg(
  x,
  cp.type = c(1, 2)[1],
  phi = 0.5,
  thr = NULL,
  trim = NULL,
  height = NULL,
  tau = NULL,
  temporal = TRUE,
  scales = NULL,
  diag = FALSE,
  B = 1000,
  q = 0.01,
  do.parallel = 4
)

Arguments

x

input data matrix, with each row representing the component time series
cp.type

cp.type = 1 specifies change points in the mean, cp.type = 2 specifies change points in the second-order structure
phi

choice of parameter for weights in Double CUSUM statistic; 0 <= phi <= 1 or phi = -1 allowed with the latter leading to the DC statistic combining phi = 0 and phi = 1/2, see Section 4.1 of Cho (2016) for further details
thr

pre-defined threshold values; when thr = NULL, bootstrap procedure is employed for the threshold selection; when thr != NULL and cp.type = 1, length(thr) should be one, if cp.type = 2, length(thr) should match length(scales)
trim

length of the intervals trimmed off around the change point candidates; trim = NULL activates the default choice (trim = round(log(dim(x)[2])))
height

maximum height of the binary tree; height = NULL activates the default choice (height = floor(log(dim(x)[2], 2)/2))
tau

a vector containing the scaling constant for each row of x; if tau = NULL, a data-driven choice is made which takes into account the presence of possibly multiple mean shifts and temporal dependence when temporal = TRUE
temporal

used when cp.type = 1; if temporal = FALSE, rows of x are scaled by mad estimates, if temporal = TRUE, their long-run variance estimates are used
scales

used when cp.type = 2; negative integers representing Haar wavelet scales to be used for computing nrow(x)*(nrow(x) + 1)/2 dimensional wavelet transformation of x; a small negative integer represents a fine scale
diag

used when cp.type = 2; if diag = TRUE, only changes in the diagonal elements of the autocovariance matrices are searched for
B

used when is.null(thr); number of bootstrap samples for threshold selection
q

used when is.null(thr); indicates the quantile of bootstrap test statistics to be used for threshold selection
do.parallel

used when is.null(thr); number of copies of R running in parallel, if do.parallel = 0, %do% operator is used, see also foreach

Value

S3 bin.tree object, which contains the following fields:

tree

a list object containing information about the nodes at which change points are detected
mat

matrix concatenation of the nodes of tree
ecp

estimated change points
thr

threshold used to construct the tree

References

H. Cho (2016) change point detection in panel data via double CUSUM statistic. Electronic Journal of Statistics, vol. 10, pp. 2000–2038.

Examples

x <- matrix(rnorm(10*100), nrow = 10)
dcbs.alg(x, cp.type = 1, phi=.5, temporal = FALSE, do.parallel = 0)$ecp

x <- matrix(rnorm(100*300), nrow = 100)
x[1:10, 151:300] <- x[1:10, 151:300] + 1
dcbs.alg(x, cp.type = 1, phi=-1, temporal = FALSE, do.parallel = 0)$ecp

Bootstrapping for threshold selection in DCBS algorithm

Description

Generate thresholds for DCBS algorithm via bootstrapping

Usage

dcbs.thr(
  z,
  interval = c(1, dim(z)[2]),
  phi = 0.5,
  cp.type = 1,
  do.clean.cp = FALSE,
  temporal = TRUE,
  scales = NULL,
  diag = FALSE,
  sgn = NULL,
  B = 1000,
  q = 0.01,
  do.parallel = 4
)

Arguments

z

input data matrix, with each row representing the component time series
interval

a vector of two containing the start and the end points of the interval from which the bootstrap test statistics are to be calculated
phi, cp.type, temporal, scales, diag, B, q, do.parallel

see dcbs.alg
do.clean.cp

if do.clean.cp = TRUE pre-change point cleaning is performed
sgn

if diag = FALSE, wavelet transformations of the cross-covariances are computed with the matching signs

Value

a numeric value for the threshold

Sparsified Binary Segmentation

Description

Perform the Sparsified Binary Segmentation algorithm detecting change-points in the mean or second-order structure of the data.

Usage

sbs.alg(
  x,
  cp.type = c(1, 2)[1],
  thr = NULL,
  trim = NULL,
  height = NULL,
  tau = NULL,
  temporal = TRUE,
  scales = NULL,
  diag = FALSE,
  B = 1000,
  q = 0.01,
  do.parallel = 4
)

Arguments

x

input data matrix, with each row representing the component time series
cp.type

cp.type = 1 specifies change-points in the mean, cp.type = 2 specifies change-points in the second-order structure
thr

pre-defined threshold values; when thr = NULL, bootstrap procedure is employed for the threshold selection; when thr != NULL and cp.type = 1, length(thr) should match nrow(x), if cp.type = 2, length(thr) should match nrow(x)*(nrow(x)+1)/2*length(scales)
trim

length of the intervals trimmed off around the change-point candidates; trim = NULL activates the default choice (trim = round(log(dim(x)[2])))
height

maximum height of the binary tree; height = NULL activates the default choice (height = floor(log(dim(x)[2], 2)/2))
tau

a vector containing the scaling constant for each row of x; if tau = NULL, a data-driven choice is made which takes into account the presence of possibly multiple mean shifts and temporal dependence when temporal = TRUE
temporal

used when cp.type = 1; if temporal = FALSE, rows of x are scaled by mad estimates, if temporal = TRUE, their long-run variance estimates are used
scales

used when cp.type = 2; negative integers representing Haar wavelet scales to be used for computing nrow(x)*(nrow(x)+1)/2 dimensional wavelet transformation of x; a small negative integer represents a fine scale
diag

used when cp.type = 2; if diag = TRUE, only changes in the diagonal elements of the autocovariance matrices are searched for
B

used when is.null(thr); number of bootstrap samples for threshold selection
q

used when is.null(thr); quantile of bootstrap test statistics to be used for threshold selection
do.parallel

used when is.null(thr); number of copies of R running in parallel, if do.parallel = 0, %do% operator is used, see also foreach

Value

S3 bin.tree object, which contains the following fields:

tree

a list object containing information about the nodes at which change-points are detected
mat

matrix concatenation of the nodes of tree
ecp

estimated change-points
thr

threshold used to construct the tree

References

H. Cho and P. Fryzlewicz (2014) Multiple-change-point detection for high dimensional time series via sparsified binary segmentation. JRSSB, vol. 77, pp. 475–507.

Examples

x <- matrix(rnorm(20*300), nrow = 20)
sbs.alg(x, cp.type = 2, scales = -1, diag = TRUE, do.parallel = 0)$ecp

x <- matrix(rnorm(100*300), nrow = 100)
x[1:10, 151:300] <- x[1:10, 151:300]*sqrt(2)
sbs.alg(x, cp.type = 2, scales = -1, diag = TRUE, do.parallel = 0)$ecp

Bootstrapping for threshold selection in SBS algorithm

Description

Generate thresholds for SBS algorithm via bootstrapping

Usage

sbs.thr(
  z,
  interval = c(1, dim(z)[2]),
  cp.type = 1,
  do.clean.cp = TRUE,
  scales = NULL,
  diag = FALSE,
  sgn = NULL,
  B = 1000,
  q = 0.01,
  do.parallel = 4
)

Arguments

z

input data matrix, with each row representing the component time series
interval

a vector of two containing the start and the end points of the interval from which the bootstrap test statistics are to be calculated
cp.type, scales, diag, B, q, do.parallel

see sbs.alg
do.clean.cp

if do.clean.cp = TRUE pre-change-point cleaning is performed
sgn

if diag = FALSE, wavelet transformations of the cross-covariances are computed with the matching signs

Value

if cp.type = 1, a vector of length nrow(z), each containing the threshold applied to the CUSUM statistics from the corresponding coordinate of z if cp.type = 2, a vector of length length(scales)*nrow(z) (when diag = TRUE) or length(scales)*nrow(z)*(nrow(z)+1)/2 (when diag = FALSE), each containing the threshold applied to the CUSUM statistics of the corresponding wavelet transformation of z