Need help with prophet?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

13.5K Stars 3.9K Forks MIT License 694 Commits 150 Opened issues


Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

Services available


Need anything else?

Contributors list

Prophet: Automatic Forecasting Procedure

Build Pypi_Version Conda_Version CRAN status

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.

Prophet is open source software released by Facebook's Core Data Science team. It is available for download on CRAN and PyPI.

Important links

  • Homepage:
  • HTML documentation:
  • Issue tracker:
  • Source code repository:
  • Contributing:
  • Prophet R package:
  • Prophet Python package:
  • Release blogpost:
  • Prophet paper: Sean J. Taylor, Benjamin Letham (2018) Forecasting at scale. The American Statistician 72(1):37-45 (

Installation in R

Prophet is a CRAN package so you can use


After installation, you can get started!

Experimental backend - cmdstanr

You can also choose an experimental alternative stan backend called

. Once you've installed
, follow these instructions to use
instead of
as the backend:
# R
# We recommend running this is a fresh R session or restarting your current session
install.packages(c("cmdstanr", "posterior"), repos = c("", getOption("repos")))

If you haven't installed cmdstan before, run:


Otherwise, you can point cmdstanr to your cmdstan path:

cmdstanr::set_cmdstan_path(path = )

Set the R_STAN_BACKEND environment variable



On Windows, R requires a compiler so you'll need to follow the instructions provided by

. The key step is installing Rtools before attempting to install the package.

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

to install it. From v0.6 onwards, Python 2 is no longer supported. As of v1.0, the package name on PyPI is "prophet"; prior to v1.0 it was "fbprophet".
# Install pystan with pip before using pip to install prophet
# pystan>=3.0 is currently not supported
pip install pystan==

pip install prophet

The default dependency that Prophet has is

. PyStan has its own installation instructions. Install pystan with pip before using pip to install prophet.

Experimental backend - cmdstanpy

You can also choose a (more experimental) alternative stan backend called

. It requires the CmdStan command line interface and you will have to specify the environment variable
pointing to it, for example:
# bash
$ CMDSTAN=/tmp/cmdstan-2.22.1 STAN_BACKEND=CMDSTANPY pip install prophet

Note that the

variable is directly related to
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 prophet

After installation, you can get started!

If you upgrade the version of PyStan installed on your system, you may need to reinstall prophet (see here).



conda install gcc
to set up gcc. The easiest way to install Prophet is through conda-forge:
conda install -c conda-forge prophet


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 prophet, and at least 2GB of memory to use prophet.


Version 1.0 (2021.03.28)

  • Python package name changed from fbprophet to prophet
  • Fixed R Windows build issues to get latest version back on CRAN
  • Improvements in serialization, holidays, and R timezone handling
  • Plotting improvements

Version 0.7 (2020.09.05)

  • Built-in json serialization
  • Added "flat" growth option
  • Bugfixes related to
  • Plotting improvements
  • Improvements in cross validation, such as parallelization and directly specifying cutoffs

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)
  • cmdstanpy
    backend 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
  • Bugfixes

Version 0.4 (2018.12.18)

  • Added holidays functionality
  • Bugfixes

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
  • Bugfixes

Version 0.2.1 (2017.11.08)

  • Bugfixes

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
  • Bugfixes

Version 0.1.1 (2017.04.17)

  • Bugfixes
  • 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.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.