wild.binary.segmentation: wild.binary.segmentation

In HDcpDetect: Detect Change Points in Means of High Dimensional Data

Description

Detects change points in time series data using the wild binary segmentation algorithm from Fryzlewicz (2014).

Usage

wild.binary.segmentation(data_M,minsize=15,num_intervals=1250,M_threshold=0.05)

Arguments

data_M	An nxp matrix representing a times series of length n with p dimensions.
minsize	The minimum interval length.
num_intervals	The number of random intervals to be generated and tested for change points.
M_threshold	Value used as a threshold to estimate temporal dependence by determining how small of a standardized difference is indistinguishable from zero.

Details

Increasing the minimum interval length will generally reduce type I error while increasing type II error.

Value

The returned value is a list of the estimated change point locations.

Author(s)

Jun Li, Jeffrey Okamoto, and Natasha Stewart

References

Li, J., Li, L., Xu, M., Zhong, P (2018). Change Point Detection in the Mean of High-Dimensional Time Series Data under Dependence. Manuscript. Fryzlewicz, P. (2014). Wild Binary Segmentation for Multiple Change-point Detection. The Annals of Statistics.

Examples

library(HDcpDetect)
HAPT2 <- as.matrix(HAPT[1:35,])
wild.binary.segmentation(HAPT2)

HDcpDetect documentation built on May 2, 2019, 7:24 a.m.