Nixtla / neuralforecast
Scalable and user friendly neural :brain: forecasting algorithms.
Why this job: topics: neural-network, transformer, deep-learning, machine-learning, pytorch
What it does
- Fast and accurate implementations of more than 30 state-of-the-art models. See the entire collection here.
- Support for exogenous variables and static covariates.
- Interpretability methods for trend, seasonality and exogenous components.
- Probabilistic Forecasting with adapters for quantile losses and parametric distributions.
- Train and Evaluation Losses with scale-dependent, percentage and scale independent errors, and parametric likelihoods.
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit yesterday
- Stars
- 4,314
- +5 this week
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v3.3.1
- Released 2 days ago
- Built with
- Python
- 86% of the code
Is it looked after?
- Contributors
- 19
- Last 90 days
- Issues
- 91% closed
- 65 still open
- Pull requests
- 713 merged
- 31 waiting
- Releases
- 41
- 12 in the last year
- Contributing guide (opens in new tab)
- No security policy
- Code of conduct (opens in new tab)
More: languages, topics and project record
Languages
- Python 86%
- Jupyter Notebook 14%
- Makefile <1%
- Shell <1%
Topics
- deep-learning
- forecasting
- esrnn
- nbeats
- nbeatsx
- time-series
- pytorch
- transformer
- nhits
- neural-network
- machine-learning
- deep-neural-networks
Project record
- JobAI libraries and models
- StatusFeatured
- Commits1,449
- Forks531
- Watching36
- Stars in 28 days+31
- CreatedApr 26, 2021
- First seen by usNov 7, 2026
- CheckedNov 12, 2026