torchcast

torchcast is a Python package for forecasting built on top of PyTorch <http://pytorch.org>_. Its benefits include:

1. An API designed around training and forecasting with batches of time-series, rather than training separate models for one time-series at a time.
2. Robust support for multivariate time-series, where multiple correlated measures are being forecasted.
3. Forecasting models that are hybrids: they are classic state-space models with the twist that every part is differentiable and can take advantage of PyTorch's flexibility. For example <https://docs.strong.io/torchcast/examples/electricity.html#Training-our-Hybrid-Forecasting-Model>_, we can use arbitrary PyTorch torch.nn.Modules to learn seasonal variations across multiple groups, embedding complex seasonality into lower-dimensional space.

This repository is the work of Strong <https://www.strong.io/>_.

.. image:: docs/examples_air_quality_6_2.png

Getting Started

torchcast can be installed with pip:

.. code-block:: bash

pip install git+https://github.com/onesixsolutions/torchcast.git#egg=torchcast

torchcast requires Python >= 3.9 and PyTorch >= 1.12.

See the Quick Start <https://docs.strong.io/torchcast/quick_start.html>_ for a simple example that will get you up to speed, or delve into the examples <https://docs.strong.io/torchcast/examples/examples.html>_ or the API <https://docs.strong.io/torchcast/api/api.html>_.