# wbsts-package: Multiple change-point detection for nonstationary time series In wbsts: Multiple Change-Point Detection for Nonstationary Time Series

## Description

Implements the Wild Binary Segmentation method of Fryzlewicz (2014) for nostationary time series as described in Korkas and Fryzlewicz (2017). Its purpose is the estimation of the number and locations of the change-points in a time series utilising the wavelet periodogram.

## Author(s)

K. Korkas and P. Fryzlewicz

## References

P. Fryzlewicz (2014), Wild Binary Segmentation for multiple change-point detection. Annals of Statistics, 42, 2243-2281. (http://stats.lse.ac.uk/fryzlewicz/wbs/wbs.pdf)

K. Korkas and P. Fryzlewicz (2017), Multiple change-point detection for non-stationary time series using Wild Binary Segmentation. Statistica Sinica, 27, 287-311. (http://stats.lse.ac.uk/fryzlewicz/WBS_LSW/WBS_LSW.pdf)

## Examples

 ```1 2 3 4 5 6 7 8 9``` ```#### Generate a highly persistent time series with changing variance and of length 5,000 ###Location of the change-points #cps=seq(from=1000,to=2800,by=200) #y=sim.pw.arma(N =3000,sd_u = c(1,1.5,1,1.5,1,1.5,1,1.5,1,1.5,1), #b.slope=rep(0.99,11),b.slope2 = rep(0.,11), mac = rep(0.,11),br.loc = cps)[[2]] ###Estimate the change points via Binary Segmentation #wbs.lsw(y,M=1)\$cp.aft ###Estimate the change points via Wild Binary Segmentation #wbs.lsw(y,M=0)\$cp.aft ```

wbsts documentation built on July 1, 2020, 5:23 p.m.