changepoint-online 1.0.0

A Collection of Methods for Online Changepoint Detection

changepoint_online

A Collection of Methods for Online Changepoint Detection

The changepoint_online package provides efficient algorithms for detecting changes in data streams based on the Focus algorithm. The Focus algorithm solves the CUSUM likelihood-ratio test exactly in O(log(n)) time per iteration, where n represents the current iteration. The method is equivalent to running a rolling window (MOSUM) simultaneously for all sizes of windows or the Page-CUSUM for all possible values of the size of change (an infinitely dense grid).

Installing the changepoint_online python package

using pip

installing from github with pip

python -m pip install 'git+https://github.com/grosed/changepoint_online/#egg=changepoint_online&subdirectory=python/package'

Key Features

• Contains all focus exponential family algorithms as well as the NPFOCuS algorithm for non-parametric changepoint detection.

• It’s versatile enough to be applied in scenarios where the pre-change parameter is either known or unknown.

• It is possible to apply constraints to detect specific types of changes (such as increases or decreases in parameter values).

References

Fast online changepoint detection via functional pruning CUSUM statistics G Romano, IA Eckley, P Fearnhead, G Rigaill - Journal of Machine Learning Research, 2023

A Constant-per-Iteration Likelihood Ratio Test for Online Changepoint Detection for Exponential Family Models K Ward, G Romano, I Eckley, P Fearnhead - Statistics and Computing, 2024

A log-linear non-parametric online changepoint detection algorithm based on functional pruning. G Romano, IA Eckley, P Fearnhead, IEEE Transactions on Signal Processing, 2024

Quick Example

### Simple gaussian change in mean case ###
from changepoint_online import Focus, Gaussian
import numpy as np

np.random.seed(0)
Y = np.concatenate((np.random.normal(loc=0.0, scale=1.0, size=5000), np.random.normal(loc=10.0, scale=1.0, size=5000)))

detector = Focus(Gaussian())
threshold = 10.0
for y in Y:
    detector.update(y)
    if detector.statistic() >= threshold:
        break

result = detector.changepoint()
print(f"We detected a changepoint at time {result['stopping_time']}.")

We detected a changepoint at time 5001.

License

Copyright (C) 2023 Gaetano Romano, Daniel Grose

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/gpl-3.0.txt.

GitHub Repository

Source files for the packages can be found at https://github.com/grosed/changepoint_online