[sd2k]

I started writing this library a while ago after I couldn't find a time series library that really fit my needs (either in Rust or elsewhere, really). Specifically I wanted something that was:

- fast

- portable (I want to be able to run it in the browser, in Python, and in Rust)

- maintainable (please don't write libraries in Jupyter notebooks...)

Augurs meets all three of those for me, and was a lot of fun to write. Plus it has a fun name and I managed to nab the augu.rs domain.

A few things I think are cool:

- the MSTL/ETS implementation is very snappy and can produce forecasts in well under 100ms for quite large time series. This was the initial motivation for writing the library.

- the Prophet implementation includes the option to use a WASM component which wraps the Stan Bayesian framework compiled to WebAssembly, run inside Wasmtime, making it an easy-to-deploy single binary. Interestingly the benchmarks show the Wasmtime version running almost as fast as the native version.

- the whole library can be run in the browser using the npm library - see the demo below for an example

Docs: https://docs.augu.rs

Demo: https://demo.augu.rs

GitHub: https://github.com/grafana/augurs