Twitter's Anomaly Detection in Pure Python

Easier CUSUM control charts. Returns simple CUSUM statistics, CUSUMs with control limit calculations, and function to generate faceted CUSUM Control Charts

Different flavours of CUSUM for change point detection.

Calibrated simulation and detectors for MES-embedded carbon-intensity monitoring of energy anomalies in machining-style processes. Characterizes the adaptive-baseline inertia blind spot and proposes an event-anchored + residual-CUSUM detector that closes it. Reproducible: code, raw sweep data, and figure scripts.

IMU fault detection

NASA Bearing Dataset: Fault Detection with Wiener denoising and custom time-frequency btstft Transforms

Statistical volume anomaly detection for trade streams - Hawkes process, CUSUM, and Bayesian Online Changepoint Detection (BOCPD). Zero dependencies. TypeScript.

Fast Online Changepoint Detection via Functional Pruning CUSUM statistics

Social Networks Monitoring

Quickest Change Detection for Unnormalized Statistical Models

Anomaly Detection in Sensor Data (LIT101) from Secure Water Treatment (SWaT) testbed . Demo of CUSUM and MLP methodologies.

CUSUM is the cumulative sum of the samples and CUMEAN is the cumulative sum of the updated samples with their mean. CUSUM and CUMEAN can detect relatively small changes in a process mean. They can be more useful in the time series dataset.

This repository represents additional control charts, various plans and variables that are used within the chart scope using Minitab software

NCIs Project 2024/25

Changepoint detection toolkit for offline and online in Rust with Python bindings

Streaming anomaly detection in Rust — detectors, calibration, SOC triage. Powers eBPFsentinel

A Python library to address the Change Detection problem using the CUSUM and CPM methods, implemented with NumPy and SciPy. The CPM implementation closely matches the R version, providing a solid alternative for Python users.

Statistical process control and process capability analysis of a pretzel manufacturing system using control charts, hypothesis testing, and Six Sigma methods.

Synthetic EHM analytics project — ISA corrections, GPA degradation modelling, CUSUM anomaly detection, and XGBoost/LSTM RUL prediction for a 20-engine turbofan fleet.