Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
Prophet is open source software released by Facebook's Core Data Science team. It is available for download on CRAN and PyPI.
Prophet alternatives and similar packages
Based on the "Machine Learning" category
* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest. Visit our partner's website for more details.
Do you think we are missing an alternative of Prophet or a related project?
Prophet: Automatic Forecasting Procedure
Prophet is a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.
- Homepage: https://facebook.github.io/prophet/
- HTML documentation: https://facebook.github.io/prophet/docs/quick_start.html
- Issue tracker: https://github.com/facebook/prophet/issues
- Source code repository: https://github.com/facebook/prophet
- Prophet R package: https://cran.r-project.org/package=prophet
- Prophet Python package: https://pypi.python.org/pypi/fbprophet/
- Release blogpost: https://research.fb.com/prophet-forecasting-at-scale/
- Prophet paper: Sean J. Taylor, Benjamin Letham (2018) Forecasting at scale. The American Statistician 72(1):37-45 (https://peerj.com/preprints/3190.pdf).
Installation in R
Prophet is a CRAN package so you can use
install.packages. For OSX, be sure to specify a source install:
# R > install.packages('prophet', type="source")
After installation, you can get started!
If you have custom Stan compiler settings, install from source rather than the CRAN binary.
Installation in Python
Prophet is on PyPI, so you can use pip to install it:
# bash $ pip install fbprophet
The major dependency that Prophet has is
pystan. PyStan has its own installation instructions. Install pystan with pip before using pip to install fbprophet.
After installation, you can get started!
If you upgrade the version of PyStan installed on your system, you may need to reinstall fbprophet (see here).
conda install gcc to set up gcc. The easiest way to install Prophet is through conda-forge:
conda install -c conda-forge fbprophet.
On Windows, PyStan requires a compiler so you'll need to follow the instructions. The easiest way to install Prophet in Windows is in Anaconda.
Make sure compilers (gcc, g++, build-essential) and Python development tools (python-dev, python3-dev) are installed. In Red Hat systems, install the packages gcc64 and gcc64-c++. If you are using a VM, be aware that you will need at least 4GB of memory to install fbprophet, and at least 2GB of memory to use fbprophet.
Version 0.5 (2019.05.14)
- Conditional seasonalities
- Improved cross validation estimates
- Plotly plot in Python
Version 0.4 (2018.12.18)
- Added holidays functionality
Version 0.3 (2018.06.01)
- Multiplicative seasonality
- Cross validation error metrics and visualizations
- Parameter to set range of potential changepoints
- Unified Stan model for both trend types
- Improved future trend uncertainty for sub-daily data
Version 0.2.1 (2017.11.08)
Version 0.2 (2017.09.02)
- Forecasting with sub-daily data
- Daily seasonality, and custom seasonalities
- Extra regressors
- Access to posterior predictive samples
- Cross-validation function
- Saturating minimums
Version 0.1.1 (2017.04.17)
- New options for detecting yearly and weekly seasonality (now the default)
Version 0.1 (2017.02.23)
- Initial release
Prophet is licensed under the [MIT license](LICENSE.md).
*Note that all licence references and agreements mentioned in the Prophet README section above are relevant to that project's source code only.