NeuralProphet
A Neural Network based Time-Series model, heavily inspired by Facebook Prophet and AR-Net.
For a complete introduction to NeuralProphet, view the presentation given at Facebook Forecasting Summit (Oct 05, 2020).
Modelling Capabilities and Development Timeline
For details, please view the Development Timeline.
Install
After downloading the code repository (via git clone), change to the repository directory (cd neural_prophet) and install neuralprophet as python package with
pip install [-e] .
Including the optional -e flag will install neuralprophet in "editable" mode, meaning that instead of copying the files into your virtual environment, a symlink will be created to the files where they are.
Now in any notebook you can do:
from neuralprophet.neural_prophet import NeuralProphet
Contribute
As far as possible, we follow the Google Python Style Guide
As for Git practices, please follow the steps described at Swiss Cheese for how to git-rebase-squash when working on a forked repo.
Authors
The alpha-stage NeuralProphet was developed by Oskar Triebe, advised by Ram Rajagopal (Stanford University) and Nikolay Laptev (Facebook, Inc), and was funded by Total S.A. We are now further developing the beta-stage package in collaboration with Hansika Hewamalage, who is advised by Christoph Bergmeir (Monash University). If you are interested in joining the project, please feel free to reach out to me (Oskar) - you can find my email on the AR-Net Paper.