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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.
Time series forecasting with PyTorch
| Date | Stars |
|---|---|
| 2026-07-24 | 4959 |
| 2026-07-25 | 4959 |
| 2026-07-28 | 4959 |
| 2026-07-30 | 4959 |
| 2026-08-06 | 4959 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
15.0
growth rate 0.00%/day
 _PyTorch Forecasting_ is a PyTorch-based package for forecasting with state-of-the-art deep learning architectures. It provides a high-level API and uses [PyTorch Lightning](https://pytorch-lightning.readthedocs.io/) to scale training on GPU or CPU, with automatic logging. | | **[Documentation](https://pytorch-forecasting.readthedocs.io)** · **[Tutorials](https://pytorch-forecasting.readthedocs.io/en/latest/tutorials.html)** · **[Release Notes](https://pytorch-forecasting.readthedocs.io/en/latest/CHANGELOG.html)** | |---|---| | **Open Source** | [](https://github.com/sktime/pytorch-forecasting/blob/master/LICENSE) [](https://gc-os-ai.github.io/) | | | **Community** | [](https://discord.com/invite/54ACzaFsn7) [](https://www.linkedin.com/company/scikit-time/) | | **CI/CD** | [](https://github.com/sktime/pytorch-forecasting/actions/workflows/pypi_release.yml) [](https://pytorch-forecasting.readthedocs.io) [](https://github.com/sktime/pytorch-forecasting) [![Code Coverage][coverage-image]][coverage-url] | | **Code** | [](https://pypi.org/project/pytorch-forecasting/) [](https://anaconda.org/conda-forge/pytorch-forecasting) [](https://www.python.org/) [](https://github.com/psf/black) | | **Downloads** |   [)](https://pepy.tech/project/pytorch-forecasting) | [coverage-image]: https://codecov.io/gh/sktime/pytorch-forecasting/branch/main/graph/badge.svg [coverage-url]: https://codecov.io/github/sktime/pytorch-forecasting?branch=main --- Our article on [Towards Data Science](https://towardsdatascience.com/introducing-pytorch-forecasting-64de99b9ef46) introduces the package and provides background information. PyTorch Forecasting aims to ease state-of-the-art timeseries forecasting with neural networks for real-world cases and research alike. The goal is to provide a high-level API with maximum flexibility for professionals and reasonable defaults for beginners. Specifically, the package provides - A timeseries dataset class which abstracts handling variable transformations, missing values, randomized subsampling, multiple history lengths, etc. - A base model class which provides basic training of timeseries models along with logging in TensorBoard and generic visualizations such as actual vs predictions and dependency plots - Multiple neural network architectures for timeseries forecasting that have been enhanced for real-world deployment and come with in-built interpretation capabilities - Multi-horizon timeseries metrics - Hyperparameter tuning with [optuna](https://optuna.readthedocs.io/) The package is built on [pytorch-lightning](https://pytorch-lightning.readthedocs.io/) to
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Jan Beitner
436
369
211
115
Franz Király · @sktime, @gc-os-ai
113
Aryan Saini
41
27
Felix Hirwa Nshuti · Carnegie Mellon
16
Xinyu Wu
13
13
12
Jirka Borovec · Czech Republic
8
6
eavae
4
4
4
Luke Merrick · Snowflake
4
3
Jake F
3
Vince Jankovics
2
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:8ba0ae3ece073117, topic:deep-learning, topic:pytorch
matched fp:8ba0ae3ece073117, topic:gpu