PyTorch Forecasting Documentation


Our article on Towards Data Science 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

  • Ranger optimizer for faster model training

  • Hyperparameter tuning with optuna

The package is built on PyTorch Lightning to allow training on CPUs, single and multiple GPUs out-of-the-box.

If you do not have pytorch already installed, follow the detailed installation instructions.

Otherwise, proceed to install the package by executing

pip install pytorch-forecasting

or to install via conda

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

Vist Getting started to learn more about the package and detailled installation instruction. The Tutorials section provides guidance on how to use models and implement new ones.

Indices and tables