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https://github.com/torchspatiotemporal/tsl

tsl: a PyTorch library for processing spatiotemporal data.
https://github.com/torchspatiotemporal/tsl

deep-learning gnn graph-neural-networks pytorch spatio-temporal spatio-temporal-analysis spatio-temporal-data spatio-temporal-graph spatio-temporal-prediction spatiotemporal spatiotemporal-data spatiotemporal-data-analysis spatiotemporal-forecasting temporal-data temporal-graphs

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tsl: a PyTorch library for processing spatiotemporal data.

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README 

        


    



    Torch Spatiotemporal

    
Neural spatiotemporal forecasting with PyTorch


    


    


    PyPI

    PyPI - Python Version

    

    Total downloads

    Documentation Status

    


    


    🚀 Getting Started - 📚 Documentation - 💻 Introductory notebook

    





 tsl (Torch Spatiotemporal) is a library built to accelerate research on neural spatiotemporal data processing

methods, with a focus on Graph Neural Networks.



Built upon popular libraries such as  PyTorch,  PyG (PyTorch Geometric), and  PyTorch Lightning,  tsl provides a unified and user-friendly framework for efficient neural spatiotemporal data processing, that goes from data preprocessing to model prototyping.



## 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,  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  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  tsl's straightforward data structures, seamlessly integrated with  PyTorch and  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  tsl's integration with  PyTorch Lightning. Accelerate training and inference across various hardware configurations.



* **Modular Neural Layers**   Build powerful and modular neural spatiotemporal models using  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  Hydra framework, a standard in the field. Validate and compare results confidently, promoting rigorous research in spatiotemporal data mining.



## Getting Started



Before you start using  tsl, please review the documentation 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  tsl, make sure you have installed  PyTorch (>=1.9.0) and  PyG (>=2.0.3) in your virtual environment (see [PyG installation guidelines](https://pytorch-geometric.readthedocs.io/en/latest/install/installation.html)).  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  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  tsl!



Thanks to all contributors! 🧡





  




## 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.