<div align="center">
<br><br>
<img alt="Torch Spatiotemporal" src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo_text.svg" width="85%"/>
<h3>Neural spatiotemporal forecasting with PyTorch</h3>
<hr>
<p>
<a href='https://pypi.org/project/torch-spatiotemporal/'><img alt="PyPI" src="https://img.shields.io/pypi/v/torch-spatiotemporal"></a>
<img alt="PyPI - Python Version" src="https://img.shields.io/badge/python-%3E%3D3.8-blue">
<!-- img alt="PyPI - Python Version" src="https://img.shields.io/pypi/pyversions/torch-spatiotemporal" -->
<img alt="Total downloads" src="https://static.pepy.tech/badge/torch-spatiotemporal">
<a href='https://torch-spatiotemporal.readthedocs.io/en/latest/?badge=latest'><img src='https://readthedocs.org/projects/torch-spatiotemporal/badge/?version=latest' alt='Documentation Status' /></a>
</p>
<p>
🚀 <a href="https://torch-spatiotemporal.readthedocs.io/en/latest/usage/quickstart.html">Getting Started</a> - 📚 <a href="https://torch-spatiotemporal.readthedocs.io/en/latest/">Documentation</a> - 💻 <a href="https://torch-spatiotemporal.readthedocs.io/en/latest/notebooks/a_gentle_introduction_to_tsl.html">Introductory notebook</a>
</p>
</div>
<p><img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> <b>tsl</b> <em>(Torch Spatiotemporal)</em> is a library built to accelerate research on neural spatiotemporal data processing
methods, with a focus on Graph Neural Networks.</p>
<p>Built upon popular libraries such as <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pytorch.svg" width="20px" align="center"/> <a href="https://pytorch.org"><b>PyTorch</b></a>, <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pyg.svg" width="20px" align="center"/> <a href="https://pyg.org">PyG</a> (PyTorch Geometric), and <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/lightning.svg" width="20px" align="center"/> <a href="https://www.pytorchlightning.ai/">PyTorch Lightning</a>, <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl provides a unified and user-friendly framework for efficient neural spatiotemporal data processing, that goes from data preprocessing to model prototyping.</p>
## Features
* **Create Custom Models and Datasets** Easily build your own custom models and datasets for spatiotemporal data analysis. Whether you're working with sensor networks, environmental data, or any other spatiotemporal domain, <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl's high-level APIs empower you to develop tailored solutions.
* **Access a Wealth of Existing Datasets and Models** Leverage a vast collection of datasets and models from the spatiotemporal data processing literature. Explore and benchmark against state-of-the-art baselines, and test your brand new model on widely used public datasets.
* **Handle Irregularities and Missing Data** Seamlessly manage irregularities in your spatiotemporal data streams, including missing data and variations in network structures. Ensure the robustness and reliability of your data processing pipelines.
* **Streamlined Preprocessing** Automate the preprocessing phase with <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl's methods for scaling, resampling and clustering time series. Spend less time on data preparation and focus on extracting meaningful patterns and insights.
* **Efficient Data Structures** Utilize <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl's straightforward data structures, seamlessly integrated with <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pytorch.svg" width="20px" align="center"/> PyTorch and <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pyg.svg" width="20px" align="center"/> PyG, to accelerate your workflows. Benefit from the flexibility and compatibility of these widely adopted libraries.
* **Scalability with PyTorch Lightning** Scale your computations effortlessly, from a single CPU to clusters of GPUs, with <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl's integration with <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/lightning.svg" width="20px" align="center"/> PyTorch Lightning. Accelerate training and inference across various hardware configurations.
* **Modular Neural Layers** Build powerful and modular neural spatiotemporal models using <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl's collection of specialized layers. Create architectures with ease, leveraging the flexibility and extensibility of the library.
* **Reproducible Experiments** Ensure experiment reproducibility using the <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/hydra.svg" width="25px" align="center"/> <a href="https://hydra.cc/">Hydra</a> framework, a standard in the field. Validate and compare results confidently, promoting rigorous research in spatiotemporal data mining.
## Getting Started
Before you start using <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl, please review the <a href="https://torch-spatiotemporal.readthedocs.io/en/latest/">documentation</a> to get an understanding of the library and its capabilities.
You can also explore the examples provided in the `examples` directory to see how train deep learning models working with spatiotemporal data.
## Installation
Before installing <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl, make sure you have installed <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pytorch.svg" width="20px" align="center"/> <a href="https://pytorch.org">PyTorch</a> (>=1.9.0) and <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pyg.svg" width="20px" align="center"/> <a href="https://pyg.org">PyG</a> (>=2.0.3) in your virtual environment (see [PyG installation guidelines](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html)). <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl is available for Python>=3.8. We recommend installation from github to be up-to-date with the latest version:
```bash
pip install git+https://github.com/TorchSpatiotemporal/tsl.git
```
Alternatively, you can install the library from the pypi repository:
```bash
pip install torch-spatiotemporal
```
To avoid dependencies issues, we recommend using [Anaconda](https://www.anaconda.com/) and the provided environment configuration by running the command:
```bash
conda env create -f conda_env.yml
```
## Tutorial
The best way to start using <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl is by following the tutorial notebook in `examples/notebooks/a_gentle_introduction_to_tsl.ipynb`.
## Documentation
Visit the [documentation](https://torch-spatiotemporal.readthedocs.io/en/latest/) to learn more about the library, including detailed API references, examples, and tutorials.
The documentation is hosted on [readthedocs](https://torch-spatiotemporal.readthedocs.io/en/latest/). For local access, you can build it from the `docs` directory.
## Contributing
Contributions are welcome! For major changes or new features, please open an issue first to discuss your ideas. See the [Contributing guidelines](https://github.com/TorchSpatiotemporal/tsl/blob/dev/.github/CONTRIBUTING.md) for more details on how to get involved. Help us build a better <img src="https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg" width="25px" align="center"/> tsl!
Thanks to all contributors! 🧡
<a href="https://github.com/TorchSpatiotemporal/tsl/graphs/contributors">
<img src="https://contrib.rocks/image?repo=TorchSpatiotemporal/tsl" />
</a>
## Citing
If you use Torch Spatiotemporal for your research, please consider citing the library
```latex
@software{Cini_Torch_Spatiotemporal_2022,
author = {Cini, Andrea and Marisca, Ivan},
license = {MIT},
month = {3},
title = {{Torch Spatiotemporal}},
url = {https://github.com/TorchSpatiotemporal/tsl},
year = {2022}
}
```
By [Andrea Cini](https://andreacini.github.io/) and [Ivan Marisca](https://marshka.github.io/).
## License
This project is licensed under the terms of the MIT license. See the [LICENSE](https://github.com/TorchSpatiotemporal/tsl/blob/main/LICENSE) file for details.
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Whether you're working with sensor networks, environmental data, or any other spatiotemporal domain, <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl's high-level APIs empower you to develop tailored solutions.\n\n* **Access a Wealth of Existing Datasets and Models** Leverage a vast collection of datasets and models from the spatiotemporal data processing literature. Explore and benchmark against state-of-the-art baselines, and test your brand new model on widely used public datasets.\n\n* **Handle Irregularities and Missing Data** Seamlessly manage irregularities in your spatiotemporal data streams, including missing data and variations in network structures. Ensure the robustness and reliability of your data processing pipelines.\n\n* **Streamlined Preprocessing** Automate the preprocessing phase with <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl's methods for scaling, resampling and clustering time series. Spend less time on data preparation and focus on extracting meaningful patterns and insights.\n\n* **Efficient Data Structures** Utilize <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl's straightforward data structures, seamlessly integrated with <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pytorch.svg\" width=\"20px\" align=\"center\"/> PyTorch and <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pyg.svg\" width=\"20px\" align=\"center\"/> PyG, to accelerate your workflows. Benefit from the flexibility and compatibility of these widely adopted libraries.\n\n* **Scalability with PyTorch Lightning** Scale your computations effortlessly, from a single CPU to clusters of GPUs, with <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl's integration with <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/lightning.svg\" width=\"20px\" align=\"center\"/> PyTorch Lightning. Accelerate training and inference across various hardware configurations.\n\n* **Modular Neural Layers** Build powerful and modular neural spatiotemporal models using <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl's collection of specialized layers. Create architectures with ease, leveraging the flexibility and extensibility of the library.\n\n* **Reproducible Experiments** Ensure experiment reproducibility using the <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/hydra.svg\" width=\"25px\" align=\"center\"/> <a href=\"https://hydra.cc/\">Hydra</a> framework, a standard in the field. Validate and compare results confidently, promoting rigorous research in spatiotemporal data mining.\n\n## Getting Started\n\nBefore you start using <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl, please review the <a href=\"https://torch-spatiotemporal.readthedocs.io/en/latest/\">documentation</a> to get an understanding of the library and its capabilities.\n\nYou can also explore the examples provided in the `examples` directory to see how train deep learning models working with spatiotemporal data.\n\n## Installation\n\nBefore installing <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl, make sure you have installed <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pytorch.svg\" width=\"20px\" align=\"center\"/> <a href=\"https://pytorch.org\">PyTorch</a> (>=1.9.0) and <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/logos/pyg.svg\" width=\"20px\" align=\"center\"/> <a href=\"https://pyg.org\">PyG</a> (>=2.0.3) in your virtual environment (see [PyG installation guidelines](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html)). <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl is available for Python>=3.8. We recommend installation from github to be up-to-date with the latest version:\n\n```bash\npip install git+https://github.com/TorchSpatiotemporal/tsl.git\n```\n\nAlternatively, you can install the library from the pypi repository:\n\n```bash\npip install torch-spatiotemporal\n```\n\nTo avoid dependencies issues, we recommend using [Anaconda](https://www.anaconda.com/) and the provided environment configuration by running the command:\n\n```bash\nconda env create -f conda_env.yml\n```\n\n## Tutorial\n\nThe best way to start using <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl is by following the tutorial notebook in `examples/notebooks/a_gentle_introduction_to_tsl.ipynb`.\n\n## Documentation\n\nVisit the [documentation](https://torch-spatiotemporal.readthedocs.io/en/latest/) to learn more about the library, including detailed API references, examples, and tutorials.\n\nThe documentation is hosted on [readthedocs](https://torch-spatiotemporal.readthedocs.io/en/latest/). For local access, you can build it from the `docs` directory.\n\n## Contributing\n\nContributions are welcome! For major changes or new features, please open an issue first to discuss your ideas. See the [Contributing guidelines](https://github.com/TorchSpatiotemporal/tsl/blob/dev/.github/CONTRIBUTING.md) for more details on how to get involved. Help us build a better <img src=\"https://raw.githubusercontent.com/TorchSpatiotemporal/tsl/main/docs/source/_static/img/tsl_logo.svg\" width=\"25px\" align=\"center\"/> tsl!\n\nThanks to all contributors! \ud83e\udde1\n\n<a href=\"https://github.com/TorchSpatiotemporal/tsl/graphs/contributors\">\n <img src=\"https://contrib.rocks/image?repo=TorchSpatiotemporal/tsl\" />\n</a>\n\n## Citing\n\nIf you use Torch Spatiotemporal for your research, please consider citing the library\n\n```latex\n@software{Cini_Torch_Spatiotemporal_2022,\n author = {Cini, Andrea and Marisca, Ivan},\n license = {MIT},\n month = {3},\n title = {{Torch Spatiotemporal}},\n url = {https://github.com/TorchSpatiotemporal/tsl},\n year = {2022}\n}\n```\n\nBy [Andrea Cini](https://andreacini.github.io/) and [Ivan Marisca](https://marshka.github.io/).\n\n## License\n\nThis project is licensed under the terms of the MIT license. See the [LICENSE](https://github.com/TorchSpatiotemporal/tsl/blob/main/LICENSE) file for details.\n",
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