Our article on Towards Data Science
introduces the package and provides background information.
Pytorch Forecasting aims to ease timeseries forecasting with neural networks for both real-world cases and
research alike. 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.