PriorRangeOrderKmeans: Detect Number and Location of Change Points of Independent...

In JieGroup/offlineChange: Detect Multiple Change Points from Time Series

Description

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

Usage

PriorRangeOrderKmeans(x, prior_range_x, num_init = NULL)

Arguments

x	The data to find change points.
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 squared root of number of observations. Must be integer.

Details

The K prior ranges contain K change points, each prior range contaions one change point.

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)
PriorRangeOrderKmeans(x,prior_range_x=list(l1,l2))

JieGroup/offlineChange documentation built on Aug. 3, 2019, 8:33 a.m.