The CUSUM repository contains multiple change point detectors for sequential analysis, enabling the detection of changes in the statistical properties of time-ordered or streaming data.

1. Overview
2. CUSUM Detectors
3. Getting Started
4. Documentation
5. Examples
6. License

A change point is a point in time at which the statistical properties of a target variable—or its underlying data-generating process—undergo a significant shift. Detecting such changes is critical in domains such as finance, energy markets, healthcare, environmental monitoring, industrial processes, and online advertising. In these settings, predictive models and decision-making systems must adapt continuously to non-stationary and evolving conditions.

This project implements multiple variants of the CUSUM algorithm for change point detection, enabling robust identification of distributional shifts in sequential data.

CUSUM-based detectors are sequential algorithms designed to identify shifts in time-ordered or streaming data. They operate by incrementally processing observations—either instance by instance or in batches—and signaling a change when accumulated evidence indicates a statistically significant deviation from the expected behavior.

The implemented detectors support both:

• Batch-based detection, where change points are identified over fixed-size windows or data batches

• Instance-based detection, where each incoming observation is evaluated upon arrival

These detectors are, therefore, well suitable for both offline analysis and real-time monitoring in streaming environmnets.

Clone the repository:

git clone https://github.com/giobbu/CUSUM.git
cd CUSUM

The repository includes a Makefile for easy setup and common tasks:

# Install dependencies using uv sync
make install

# Run tests with coverage
make test

# Upgrade a specific package
make upgrade PACKAGE=<package-name>

# View all available commands
make help

Alternatively, set up manually using uv:

# Install dependencies
uv sync

# Run tests
pytest --cov=source test/

Documentation is available at CUSUM Documentation

View details on docs - Here

RLS-CUSUM based Monitoring with an instance-based linear learning model and the CUSUM detector.

At each time step:

• Generate a prediction with recursive least squares (RLS) model;
• Acquire the true observed value;
• Compute residual;
• Apply the CUSUM detector on the residuals to identify potential change points;
• Update the model parameters with the new data instance.

View details on notebook - Here

• Event-based CUSUM detection with Kafka, Prometheus and Grafana in mlops-kafka
• Scheduled CUSUM detection with Kubernetes CronJob and minikube local cluster in mlops-cronjob

This project is under the GPL-3.0 license.