tft-torch 0.0.6
A Python library that implements ״Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting״
Requires Python: >=3.7

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tft-torch
=====

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**tft-torch** is a ``Python`` library that
implements <cite>["Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting"][1]</cite>
using pytorch framework. The library provides a complete implementation of a time-series multi-horizon forecasting model
with state-of-the-art performance on several benchmark datasets.

This library works for Python 3.7 and higher and PyTorch 1.6.0 and higher.

Installation
------------
This library is distributed on [PyPi](https://pypi.org/project/tft-torch) and can be installed using ``pip``.

~~~~~~~~~~~~~~~~~~~~~~
$ pip install tft-torch 
~~~~~~~~~~~~~~~~~~~~~~

The command above will automatically install all the required dependencies. Please visit the
[installation](https://PlaytikaOSS.github.io/tft-torch/build/html/installation.html) page for more details.


Getting started
---------------
Check out the [tutorials](https://PlaytikaOSS.github.io/tft-torch/build/html/tutorials.html) for a demonstration
how to use the library.


Documentation
-------------
For more information, refer to our
[***blogpost***](https://www.playtika-blog.com/playtika-ai/multi-horizon-forecasting-using-temporal-fusion-transformers-a-comprehensive-overview-part-1/)
and
[complete documentation](https://PlaytikaOSS.github.io/tft-torch).

# Reference

This repository suggests an implementation of a model based on the work presented in the following paper:

```
@misc{lim2020temporal,
      title={Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting}, 
      author={Bryan Lim and Sercan O. Arik and Nicolas Loeff and Tomas Pfister},
      year={2020},
      eprint={1912.09363},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}
```

Some parts of the implementation rely on [mattsherar](https://github.com/mattsherar)'s TFT implementation, available as
part of the [Temporal_Fusion_Transform](https://github.com/mattsherar/Temporal_Fusion_Transform) repository.

[1]: https://arxiv.org/abs/1912.09363


Info for developers
-------------------

The source code of the project is available on [GitHub](https://github.com/PlaytikaOSS/tft-torch).

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
$ git clone https://github.com/PlaytikaOSS/tft-torch.git
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

You can install the library and the dependencies with one of the following commands:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
$ pip install .                        # install library + dependencies
$ pip install ".[develop]"             # install library + dependencies + developer-dependencies
$ pip install -r requirements.txt      # install dependencies
$ pip install -r requirements-dev.txt  # install developer-dependencies
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For creating the "*pip-installable*" ``*.whl`` file, run the following command (at the root of the repository):

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
$ python -m build
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

For creating the HTML documentation of the project, run the following commands:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
$ cd docs
$ make clean
$ make html
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Run tests
---------

Tests can be executed with ``pytest`` running the following commands:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
$ cd tests
$ pytest                                      # run all tests
$ pytest test_testmodule.py                   # run all tests within a module
$ pytest test_testmodule.py -k test_testname  # run only 1 test
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

License
-------

[MIT License](LICENSE)