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wbsts (version 2.1)

Multiple Change-Point Detection for Nonstationary Time Series

Description

Implements detection for the number and locations of the change-points in a time series using the Wild Binary Segmentation and the Locally Stationary Wavelet model of Korkas and Fryzlewicz (2017) .

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Version

Install

install.packages('wbsts')

Monthly Downloads

149

Version

2.1

License

GPL (>= 2)

Maintainer

Karolos Korkas

Last Published

June 12th, 2020

Functions in wbsts (2.1)

multi_across_fip

The value that maximises the random CUSUM statistic across all the scales (C++ version)
tau.fun

Universal thresholds
hello

Hello, World!
get.thres.ar

Selection of thresholds by fitting an AR(p) model
uh.wbs

The Wild Binary Segmentation algorithm
sim.pw.arma

Simulation of a piecewise constant ARMA(p,q) model for p=2 and q=1
wbs.lsw

Change point detection for a nonstationary process using Wild Binary Segmentation
post.processing

Post-processing of the change-points
finner_prod_maxp

The function finds the value which yields the maximum inner product (CUSUM) of a a time series located between \(100(1-p)\%\) and \(100p\%\) of its support
cusum

A C++ implementation of the CUSUM statistic
ews.trans

Computation of the Evolutionary Wavelet Spectrum (EWS)
get.thres

Universal thresholds calculation
across_fip

The value that maximises the CUSUM statistic across all the scales (C++ version)
sim.pw.ar

Simulation of a piecewise constant AR(1) model
wbsts-package

Multiple change-point detection for nonstationary time series
sim.pw.ar2

Simulation of a piecewise constant AR(2) model
cr.rand.max.inner.prod

The value that maximises the random CUSUM statistic across all the scales