Introduction
MTSA is a research toolkit designed to aggregate machine learning models for anomaly detection, with a strong focus on enhancing reproducibility and explainability in model implementation. It offers a structured environment for developing, testing, and comparing various anomaly detection approaches, prioritizing replicability and ease of use. The toolkit is continuously updated to include both classical and state-of-the-art algorithms for anomaly detection in multivariate time series.
- Github repository: https://github.com/IoTDataAtelier/mtsa
🤝 Contributing
Section titled “🤝 Contributing”Contributions are welcome! If you’d like to add new models, improve the documentation, or fix issues, feel free to open a pull request or create an issue.