PriorRangeOrderKmeansCpp: Detect Location of Change Points of Independent Data with...

In offlineChange: Detect Multiple Change Points from Time Series

Description

Detect the location of change points based on minimizing within segment quadratic loss with restriction of prior ranges that contaion change points.

Usage

PriorRangeOrderKmeansCpp(x, prior_range_x, num_init = 10)

Arguments

x	The data to find change points with dimension N x D, must be matrix
prior_range_x	The prior ranges that contain change points.
num_init	The number of repetition times, in order to avoid local minimal. Default is 10. Must be integer.

Details

The K change points form K+1 segments (1 2 ... change_point(1)) ... (change_point(K) ... N).

Value

num_change_point	optimal number of change points.
change_point	location of change points.

References

J. Ding, Y. Xiang, L. Shen, and V. Tarokh, Multiple Change Point Analysis: Fast Implementation and Strong Consistency. IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4495-4510, 2017.

Examples

a<-matrix(rnorm(40,mean=-1,sd=1),nrow=20,ncol=2)
b<-matrix(rnorm(120,mean=0,sd=1),nrow=60,ncol=2)
c<-matrix(rnorm(40,mean=1,sd=1),nrow=20,ncol=2)
x<-rbind(a,b,c)
l1<-c(15,25)
l2<-c(75,100)
prior_range_x<-list(l1,l2)
PriorRangeOrderKmeansCpp(x,prior_range_x=list(l1,l2))

offlineChange documentation built on April 20, 2020, 9:10 a.m.