PyTorch Forecasting Documentation#GitHub
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
To use the MQF2 loss (multivariate quantile loss), also execute
pip install git+https://github.com/KelvinKan/CP-Flow.git@package-specific-version --no-deps