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
- Contributing: https://facebook.github.io/prophet/docs/contributing.html
- 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
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. From v0.6 onwards, Python 2 is no longer supported.
# Install pystan with pip before using pip to install fbprophet pip install pystan pip install fbprophet
The default dependency that Prophet has is
pystan. PyStan has its own installation instructions. Install pystan with pip before using pip to install fbprophet.
You can also choose a (more experimental) alternative stan backend called
cmdstanpy. It requires the CmdStan command line interface and you will have to specify the environment variable
STAN_BACKEND pointing to it, for example:
# bash $ CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=CMDSTANPY pip install fbprophet
Note that the
CMDSTAN variable is directly related to
cmdstanpy module and can be omitted if your CmdStan binaries are in your
It is also possible to install Prophet with two backends:
# bash $ CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=PYSTAN,CMDSTANPY pip 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 0.6 (2020.03.03)
- Fix bugs related to upstream changes in
- Compile model during first use, not during install (to comply with CRAN policy)
cmdstanpybackend now available in Python
- Python 2 no longer supported
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.