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PyTorch Forecasting Documentation
==================================

.. raw:: html

   <a class="github-button" href="https://github.com/jdb78/pytorch-forecasting" data-icon="octicon-star" data-size="large" data-show-count="true" aria-label="Star jdb78/pytorch-forecasting on GitHub">GitHub</a>


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 both 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 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 allow
training on CPUs, single and multiple GPUs out-of-the-box.

If you do not have pytorch already installed, follow the :ref:`detailed installation instructions<install>`.

Otherwise, proceed to install the package by executing

.. code-block::

   pip install pytorch-forecasting

or to install via conda

.. code-block::

   conda install pytorch-forecasting pytorch>=1.7 -c pytorch -c conda-forge

To use the MQF2 loss (multivariate quantile loss), also execute

.. code-block::

   pip install pytorch-forecasting[mqf2]

Vist :ref:`Getting started <getting-started>` to learn more about the package and detailled installation instruction.
The :ref:`Tutorials <tutorials>` section provides guidance on how to use models and implement new ones.

.. toctree::
   :titlesonly:
   :hidden:
   :maxdepth: 6

   getting-started
   tutorials
   data
   models
   metrics
   faq
   contribute
   api
   CHANGELOG


Indices and tables
==================

* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`