torchcast

torchcast is a Python package for forecasting built on top of PyTorch. 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, 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.

Getting Started

torchcast can be installed with pip:

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

torchcast requires Python >= 3.8 and PyTorch >= 1.8.

See the Quick Start for a simple example that will get you up to speed, or delve into the examples or the API.