Nixtla / mlforecast
Scalable machine 🤖 learning for time series forecasting.
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Fastest implementations of feature engineering for time series
- Out-of-the-box compatibility with pandas, polars, spark, dask, and
- Probabilistic Forecasting with Conformal Prediction.
- Support for exogenous variables and static covariates.
- Familiar sklearn syntax. .fit and .predict.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit yesterday
- Stars
- 1,306
- +4 this week
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.1.0
- Released 4 months ago
- Built with
- Python
- 95% of the code
Is it looked after?
- Contributors
- 8
- Last 90 days
- Issues
- 92% closed
- 19 still open
- Pull requests
- 441 merged
- 18 waiting
- Releases
- 44
- 3 in the last year
- Contributing guide (opens in new tab)
- No security policy
- Code of conduct (opens in new tab)
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More: languages, topics and project record
Languages
- Python 95%
- Jupyter Notebook 5%
- Shell <1%
- Makefile <1%
Topics
- forecast
- forecasting
- machine-learning
- lightgbm
- xgboost
- dask
- python
- time-series
Project record
- JobOther AI tools
- StatusListed
- Commits546
- Forks150
- Watching9
- Stars in 28 days+20
- CreatedApr 26, 2021
- First seen by usNov 7, 2026
- CheckedNov 12, 2026