ESesafak·Feb 28, 2025
Same here. The open source space here is neglected. I just heard about Grafana's augurs: https://github.com/grafana/augurs
In Merlion: A Machine Learning Framework for Time Series Intelligencegrafana
augurs
Rust2024–2025
grafana / augurs
augurs
Time series analysis for Rust, with bindings to Python and Javascript
4Distinct people
5Mentions
0Explicit recommenders
582GitHub stars
What to know before exploring
What it is
Time series analysis for Rust, with bindings to Python and Javascript
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
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Also in these discussions
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week5 dated mentions
Observed mentions: 2024-11-19 15:20:58 UTC to 2025-02-28 20:40:39 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Nov 18 | 2024-11-18 00:00:00 UTC | 2024-11-25 00:00:00 UTC | 2 | 1 | 1 |
| Nov 25 | 2024-11-25 00:00:00 UTC | 2024-12-02 00:00:00 UTC | 0 | 0 | 0 |
| Dec 2 | 2024-12-02 00:00:00 UTC | 2024-12-09 00:00:00 UTC | 0 | 0 | 0 |
| Dec 9 | 2024-12-09 00:00:00 UTC | 2024-12-16 00:00:00 UTC | 0 | 0 | 0 |
| Dec 16 | 2024-12-16 00:00:00 UTC | 2024-12-23 00:00:00 UTC | 0 | 0 | 0 |
| Dec 23 | 2024-12-23 00:00:00 UTC | 2024-12-30 00:00:00 UTC | 0 | 0 | 0 |
| Dec 30 | 2024-12-30 00:00:00 UTC | 2025-01-06 00:00:00 UTC | 0 | 0 | 0 |
| Jan 6 | 2025-01-06 00:00:00 UTC | 2025-01-13 00:00:00 UTC | 1 | 0 | 1 |
| Jan 13 | 2025-01-13 00:00:00 UTC | 2025-01-20 00:00:00 UTC | 0 | 0 | 0 |
| Jan 20 | 2025-01-20 00:00:00 UTC | 2025-01-27 00:00:00 UTC | 0 | 0 | 0 |
| Jan 27 | 2025-01-27 00:00:00 UTC | 2025-02-03 00:00:00 UTC | 0 | 0 | 0 |
| Feb 3 | 2025-02-03 00:00:00 UTC | 2025-02-10 00:00:00 UTC | 0 | 0 | 0 |
| Feb 10 | 2025-02-10 00:00:00 UTC | 2025-02-17 00:00:00 UTC | 0 | 0 | 0 |
| Feb 17 | 2025-02-17 00:00:00 UTC | 2025-02-24 00:00:00 UTC | 1 | 0 | 1 |
| Feb 24 | 2025-02-24 00:00:00 UTC | 2025-03-03 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
5 sourcesSome links:
- Repo: https://github.com/grafana/augurs
- Docs: https://docs.augu.rs/
- Python library: https://pypi.org/project/augurs/
- npm library: https://www.npmjs.com/package/@bsull/augurs
In Augurs demoa colleague is doing a FOSDEM 2025 talk about https://github.com/grafana/augurs
In Time-Series Anomaly Detection: A Decade ReviewI 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
Read the rest
, 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
Show HN: Augurs, a time series toolkit for Rust
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
Read the rest
, 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