MTSA documentation
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.
Table of contents
Section titled “Table of contents”General
Section titled “General”Guides
Section titled “Guides”How to Contribute
Section titled “How to Contribute”- Fix Bugs
- Implement Features
- Report Bugs
- Write Documentation
- Submit Feedback
- Get Started!
- Pull Request Guidelines!