wbs.lsw {wbsts}

Change point detection for a nonstationary process using Wild Binary Segmentation

Description

The function returns the estimated locations of the change-points in a nonstationary time series. Currently only the Method 2 of aggregation is implemented.

Usage

wbs.lsw(y, C_i = tau.fun(y), scales = NULL, M = 0, cstar = 0.75, lambda = 0.75)

Arguments

y

The time series.

C_i

A vector of threshold parameters for different scales.

scales

The wavelet periodogram scales to be used. If NULL (DEFAULT) then this is selected as described in the main text.

M

The maximum number of random intervals drawn. If M=0 (DEFAULT) this is selected to be a linear function of the sample size of y. If M=1 then the segmentation is conducted via the Binary segmentation method.

cstar

This refers to the unbalanceness parameter c_{\star}.

lambda

This parameter defines the maximum number of the wavelet periodogam scales. This is used if scales = NULL.

Value

cp.bef

Returns the estimated change-points before post-processing

cp.aft

Returns the estimated change-points after post-processing

Author(s)

K. Korkas and P. Fryzlewicz

References

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

#### 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