Please note that the project is still in beta phase. Please report any issues you encounter or suggestions you have. We will do our best to address them quickly. Contributions are also highly welcome!
A Neural Network based Time-Series model, inspired by Facebook Prophet and AR-Net, built on PyTorch.
For a visual introduction to NeuralProphet, view the presentation given at the 40th International Symposium on Forecasting.
We are working on a documentation page. Contributions welcome!
After cloning the code repository you can install neuralprophet as regular python package:
git clone <copied link from github>
pip install neural_prophetIf you plan to use the package in a Jupyter notebook, we recommended to install the 'live' version:
pip install neural_prophet[live]This will allow you to enable plot_live_loss in the fit function to get a live plot of train (and validation) loss.
from neuralprophet import NeuralProphetAfter importing the package, you can use NeuralProphet in your code:
m = NeuralProphet()
metrics = m.fit(df, freq="D")
future = m.make_future_dataframe(df, periods=30)
forecast = m.predict(future)You can visualize your results with the inbuilt plotting functions:
fig_forecast = m.plot(forecast)
fig_components = m.plot_components(forecast)
fig_model = m.plot_parameters()- Autocorrelation modelling through AR-Net
- Piecewise linear trend
- Fourier term Seasonality at different periods such as yearly, daily, weekly, hourly.
- Lagged regressors
- Future regressors
- Holidays & special events
- Sparsity of coefficients through regularization
- Plotting for forecast components, model coefficients as well as final forecasts
After downloading the code repository (via git clone), change to the repository directory (cd neural_prophet), activate your virtual environment, and install neuralprophet as python package with
pip install -e .[dev]
(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.)
Additionally you must run $ neuralprophet_dev_setup in your console to run the dev-setup script which installs appropriate git hooks for Black and Unittests.
We deploy Black, the uncompromising code formatter, so there is no need to worry about style. Beyond that, where reasonable, for example for docstrings, 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.
For details, please view the Development Timeline.
The next versions of NeuralProphet are expected to cover a set of new exciting features:
- Robustify training for different datasets
- Logistic growth for trend component.
- Uncertainty estimation of individual forecast components as well as the final forecasts.
- Support for panel data by building global forecasting models.
- Incorporate time series featurization for improved forecast accuracy.
- soft-start regularization
- confidence interval for forecast (as quantiles via pinball loss)
- documentation pages added
- 1cycle policy
- learning rate range test
- tutorial notebooks: trend, events
- fixes to plotting, changepoints
The project efford is led by Oskar Triebe (Stanford University), advised by Nikolay Laptev (Facebook, Inc) and Ram Rajagopal (Stanford University) and has been partially funded by Total S.A. The project has been developed in close collaboration with Hansika Hewamalage, who is advised by Christoph Bergmeir (Monash University).
This is the list of NeuralProphet's significant contributors. This does not necessarily list everyone who has contributed code. To see the full list of contributors, see the revision history in source control.
- Oskar Triebe
- Hansika Hewamalage
- Nikolay Laptev
- Riley Dehaan
- Gonzague Henri
- Ram Rajagopal
- Christoph Bergmeir
- Italo Lima
- Caner Komurlu
- Rodrigo Riveraca
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.