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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
NeuralProphet: A simple forecasting package
| Date | Stars |
|---|---|
| 2026-07-24 | 4292 |
| 2026-07-25 | 4292 |
| 2026-07-28 | 4292 |
| 2026-07-30 | 4292 |
| 2026-08-06 | 4292 |
Today
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0.0
growth rate 0.00%/day
[](https://github.com/ourownstory/neural_prophet/releases) [](https://pypi.python.org/pypi/neuralprophet) [](https://www.python.org/) [](https://github.com/psf/black) [](https://opensource.org/licenses/MIT) [](https://github.com/ourownstory/neural_prophet/actions/workflows/tests.yml) [](https://codecov.io/gh/ourownstory/neural_prophet) [](https://neuralprophet.slack.com/join/shared_invite/zt-sgme2rw3-3dCH3YJ_wgg01IXHoYaeCg#/shared-invite/email) [](https://pepy.tech/project/neuralprophet)  Please note that the project is still in beta phase. Please report any issues you encounter or suggestions you have. We will do our best to address them quickly. Contributions are very welcome! # NeuralProphet: human-centered forecasting NeuralProphet is an easy to learn framework for interpretable time series forecasting. NeuralProphet is built on PyTorch and combines Neural Networks and traditional time-series algorithms, inspired by [Facebook Prophet](https://github.com/facebook/prophet) and [AR-Net](https://github.com/ourownstory/AR-Net). - With a few lines of code, you can define, customize, visualize, and evaluate your own forecasting models. - It is designed for iterative human-in-the-loop model building. That means that you can build a first model quickly, interpret the results, improve, repeat. Due to the focus on interpretability and customization-ability, NeuralProphet may not be the most accurate model out-of-the-box; so, don't hesitate to adjust and iterate until you like your results. - NeuralProphet is best suited for time series data that is of higher-frequency (sub-daily) and longer duration (at least two full periods/years). ## Documentation The [documentation page](https://neuralprophet.com) may not be entirely up to date. Docstrings should be reliable, please refer to those when in doubt. We are working on an improved documentation. We appreciate any help to improve and update the docs. For a visual introduction to NeuralProphet, [view this presentation](notes/NeuralProphet_Introduction.pdf). ## Contribute We compiled a [Contributing to NeuralProphet](CONTRIBUTING.md) page with practical instructions and further resources to help you become part of the family. ## Community #### Discussion and Help If you have any questions or suggestion, you can participate in [our community right here on Github](https://github.com/ourownstory/neural_prophet/discussions) #### Slack Chat We also have an active [Slack community](https://join.slack.com/t/neuralprophet/shared_invite/zt-sgme2rw3-3dCH3YJ_wgg01IXHoYaeCg). Come and join the conversation! ## Tutorials [](https://colab.research.google.com/github/ourownstory/neural_prophet) There are several [example notebooks](docs/source/tutorials) to help you get started. You can find the datasets used in the tutorials, including data preprocessing examples, in our [neuralprophet-data repository](https://github.com/ourownstory/n
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Ikko Eltociear Ashimine · Japan
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b7a9712e4946fc1f, topic:deep-learning, topic:neural-network, topic:pytorch