A new method for scalable model-free online change-point detection.

This repository contains the code for NEWMA: a new method for scalable model-free online change-point detection, Nicolas Keriven, Damien Garreau, Iacopo Poli.

To cite this work

@ARTICLE{9078835, author={N. {Keriven} and D. {Garreau} and I. {Poli}}, journal={IEEE Transactions on Signal Processing}, title={NEWMA: a new method for scalable model-free online change-point detection}, year={2020}, volume={}, number={}, pages={1-1},} 

The code is written for Python 3.
You can install the Python modules required by running pip install -r requirements.txt inside the folder.

You can install the onlinecp Python package by running pip install ./ from the root folder of this repository.

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You can generate data for the figures in the paper as follows:

• run test_dim.py and test_B_runningtime.py for Figure 4a
• run test_adaptive_vs_fixed.py for Figure 4b
• run test_algos_synthetic_data.sh for Figure 4c
• run test_algos_vad.sh for Figure 4d

The scripts to generate the plots from data are in plots and they have the same name prepended by plot_. Look at plots/README.md for info on how to run them.

The code for the older version of our paper is in code_v1. The subdirectory contains its README.md.