Detect Multiple Change Points from Time Series

Detect the number and locations of change points. The locations can be either exact or in terms of ranges, depending on the available computational resource. The method is based on Jie Ding, Yu Xiang, Lu Shen, Vahid Tarokh (2017) .

The 'offlineChange' R package

Detect Multiple Change Points from Time Series

Getting Started

First install the devtools package

install.packages("devtools")

library("devtools")

Then install this package

install_github('JieGroup/offlineChange')

Using This Package

To see the available function to use, type

ls("package:offlineChange")

A quick guide of package can be found here

Reference Papers

Ding, J., Xiang, Y., Shen, L., & Tarokh, V. (2017). Multiple change point analysis: Fast implementation and strong consistency. IEEE Transactions on Signal Processing, 65(17), 4495-4510. link

J. Ding, "Multi-window method for unsupervised learning," preprint, 2019.

Acknowledgment

This research is funded by the Defense Advanced Research Projects Agency (DARPA) under grant number HR00111890040.