HDcpDetect (version 0.1.0)

binary.segmentation: binary.segmentation

Description

Detects change points in time series data using a binary segmentation algorithm.

Usage

binary.segmentation(data_M,alpha=.05,power_enhancement=TRUE,M_threshold=0.05)

Arguments

data_M

An nxp matrix representing a times series of length n with p dimensions.

alpha

The critical value for the hypothesis testing procedure.

power_enhancement

Indicates whether to add a power enhancement term to the test statistic.

M_threshold

Value used as a threshold to estimate temporal dependence by determining how small of a standardized difference is indistinguishable from zero.

Value

The returned value is a list with the following components

Foundlist

The estimated locations of the change points

pvalues

The p values corresponding to each change point estimate

Details

The power enhancement term reduces type II error but slows the algorithm.

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.

Examples

# NOT RUN {
library(HDcpDetect)
HAPT2 <- as.matrix(HAPT[1:35,])
binary.segmentation(data_M=HAPT2,power_enhancement=FALSE)

# }