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 very welcome!
NeuralProphet is an easy to learn framework for interpretable time series forecasting. NeuralProphet is built on PyTorch and combines Neural Networks and traditional time-series algorithms, inspired by Facebook Prophet and AR-Net.
- With a few lines of code, you can define, customize, visualize, and evaluate your own forecasting models.
- It is designed for iterative human-in-the-loop model building. That means that you can build a first model quickly, interpret the results, improve, repeat. Due to the focus on interpretability and customization-ability, NeuralProphet may not be the most accurate model out-of-the-box; so, don't hesitate to adjust and iterate until you like your results.
- NeuralProphet is best suited for time series data that is of higher-frequency (sub-daily) and longer duration (at least two full periods/years).
The documentation page may not be entirely up to date. Docstrings should be reliable, please refer to those when in doubt. We are working on an improved documentation. We appreciate any help to improve and update the docs.
For a visual introduction to NeuralProphet, view this presentation.
We compiled a Contributing to NeuralProphet page with practical instructions and further resources to help you become part of the family.
If you have any questions or suggestion, you can participate in our community right here on Github
We also have an active Slack community. Come and join the conversation!
There are several example notebooks to help you get started.
You can find the datasets used in the tutorials, including data preprocessing examples, in our neuralprophet-data repository.
Please refer to our documentation page for more resources.
from neuralprophet import NeuralProphetAfter importing the package, you can use NeuralProphet in your code:
m = NeuralProphet()
metrics = m.fit(df)
forecast = m.predict(df)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()If you want to forecast into the unknown future, extend the dataframe before predicting:
m = NeuralProphet().fit(df, freq="D")
df_future = m.make_future_dataframe(df, periods=30)
forecast = m.predict(df_future)
fig_forecast = m.plot(forecast)You can now install neuralprophet directly with pip:
pip install neuralprophetIf you plan to use the package in a Jupyter notebook, we recommended to install the 'live' version:
pip install neuralprophet[live]This will allow you to enable plot_live_loss in the fit function to get a live plot of train (and validation) loss.
If you would like the most up to date version, you can instead install directly from github:
git clone <copied link from github>
cd neural_prophet
pip install .Note for Windows users: Please use WSL2.
- Autoregression: Autocorrelation modelling - linear or NN (AR-Net).
- Trend: Piecewise linear trend with optional automatic changepoint detection.
- Seasonality: Fourier terms at different periods such as yearly, daily, weekly, hourly.
- Lagged regressors: Lagged observations (e.g temperature sensor) - linear or NN.
- Future regressors: In advance known features (e.g. temperature forecast) - linear or NN.
- Events: Country holidays & recurring custom events.
- Global Modeling: Components can be local, global or 'glocal' (global + regularized local)
- Multiple time series: Fit a global/glocal model with (partially) shared model parameters.
- Uncertainty: Estimate values of specific quantiles - Quantile Regression.
- Regularize modelling components.
- Plotting of forecast components, model coefficients and more.
- Time series crossvalidation utility.
- Model checkpointing and validation.
- Cross-relation of lagged regressors.
- Static metadata regression for multiple series
- Logistic growth for trend component.
For a list of past changes, please refer to the releases page.
Please cite NeuralProphet in your publications if it helps your research:
@misc{triebe2021neuralprophet,
title={NeuralProphet: Explainable Forecasting at Scale},
author={Oskar Triebe and Hansika Hewamalage and Polina Pilyugina and Nikolay Laptev and Christoph Bergmeir and Ram Rajagopal},
year={2021},
eprint={2111.15397},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
NeuralProphet is an open-source community project, supported by awesome people like you. If you are interested in joining the project, please feel free to reach out to me (Oskar) - you can find my email on the NeuralProphet Paper.