# Augurs Time Series Toolkit for Rust > [! note]- > The content of this page is generated by audio/video transcription and text transformation from the content and links of this source. Source: [https://fosdem.org/2025/schedule/event/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust/](https://fosdem.org/2025/schedule/event/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust/) <video src="https://video.fosdem.org/2025/ub2252a/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust.av1.webm" controls></video> ## Summary & Highlights: **Summary** Augurs is a new toolkit for time series analysis developed in Rust, designed to offer functionalities such as forecasting, outlier detection, and clustering. The toolkit integrates algorithms from Python and R, along with novel ideas, to optimize machine learning processes. Augurs aims to provide performance and usability improvements and is available with bindings for JavaScript and Python. The session discusses the technical decisions made during algorithm porting, profiling, and optimization, as well as the challenges of creating WebAssembly bindings for JavaScript. **Augurs Toolkit Overview** Augurs is a comprehensive library for time series analysis written in Rust, offering tools for tasks like forecasting and clustering. It incorporates algorithms from Python and R, providing a robust solution for developers looking to perform advanced data analysis. The toolkit supports bindings for JavaScript and Python, making it versatile for various applications. It emphasizes performance optimization and offers insights into the trade-offs involved in creating WebAssembly bindings. **Technical Insights and Challenges** The session delves into the technical aspects of translating algorithms from languages like C++ and Fortran to Rust. It highlights the profiling techniques used for optimizing machine learning code and discusses the trade-offs in performance and usability when creating JavaScript/WebAssembly bindings. The speaker shares lessons learned and the importance of understanding legacy code during the translation process. **Applications and Use Cases** Augurs can be applied in various real-world scenarios, such as visualizing time series data, setting thresholds for monitoring systems, and detecting anomalies. It supports machine learning algorithms for forecasting, clustering, and outlier detection, providing a valuable tool for developers working with time series data. The session emphasizes the toolkit's ability to handle complex patterns and its potential for integration into existing systems. **Future Directions and Innovations** The session concludes with a discussion on the future potential of Augurs, including the possibility of incorporating additional algorithms and expanding its capabilities. It also touches on the challenges and opportunities presented by WebAssembly and the importance of continuous optimization and innovation in the field of time series analysis. ## Importance for an eco-social transformation Augurs has significant potential for eco-social transformation by enhancing data analysis capabilities in sustainable development projects. Its open-source nature aligns with ethical and social values, promoting transparency and community collaboration. Eco-social designers can leverage Augurs for environmental monitoring, resource management, and predictive analytics. The toolkit's ability to detect anomalies and forecast trends can aid in optimizing resource use and reducing waste. However, challenges remain in terms of accessibility and the need for technical expertise to implement and customize the toolkit. Social and political hurdles include ensuring equitable access to technology and fostering collaboration across diverse communities. ## Links [Demo](https://fosdem.org/2025/schedule/event/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust/demo) [Docs](https://fosdem.org/2025/schedule/event/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust/docs) [GitHub](https://github.com/fosdem/2025/schedule/event/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust) [Video recording (AV1/WebM)](https://video.fosdem.org/2025/ub2252a/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust.av1.webm) [Video recording (MP4)](https://video.fosdem.org/2025/ub2252a/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust.av1.mp4) [Video recording subtitle file (VTT)](https://video.fosdem.org/2025/ub2252a/fosdem-2025-4668-augurs-a-time-series-toolkit-for-rust.vtt)