2023 Update: We discuss our plans for the future of Prophet in this blog post: facebook/prophet in 2023 and beyond

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.

• 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/prophet/
• Release blogpost: https://research.facebook.com/blog/2017/2/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).

⚠️ The CRAN version of prophet is fairly outdated. To get the latest bug fixes and updated country holiday data, we suggest installing the latest release.

Prophet is a CRAN package so you can use install.packages.

install.packages('prophet')

After installation, you can get started!

install.packages('remotes')
remotes::install_github('facebook/prophet@*release', subdir = 'R')

You can also choose an experimental alternative stan backend called cmdstanr. Once you've installed prophet, follow these instructions to use cmdstanr instead of rstan as the backend:

# R
# We recommend running this in a fresh R session or restarting your current session
install.packages(c("cmdstanr", "posterior"), repos = c("https://mc-stan.org/r-packages/", getOption("repos")))

# If you haven't installed cmdstan before, run:
cmdstanr::install_cmdstan()
# Otherwise, you can point cmdstanr to your cmdstan path:
cmdstanr::set_cmdstan_path(path = <your existing cmdstan>)

# Set the R_STAN_BACKEND environment variable
Sys.setenv(R_STAN_BACKEND = "CMDSTANR")

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

Prophet is on PyPI, so you can use pip to install it.

python -m pip install prophet

• 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".
• As of v1.1, the minimum supported Python version is 3.7.

After installation, you can get started!

Prophet can also be installed through conda-forge.

conda install -c conda-forge prophet

To get the latest code changes as they are merged, you can clone this repo and build from source manually. This is not guaranteed to be stable.

git clone https://github.com/facebook/prophet.git
cd prophet/python
python -m pip install -e .

By default, Prophet will use a fixed version of cmdstan (downloading and installing it if necessary) to compile the model executables. If this is undesired and you would like to use your own existing cmdstan installation, you can set the environment variable PROPHET_REPACKAGE_CMDSTAN to False:

export PROPHET_REPACKAGE_CMDSTAN=False; python -m pip install -e .

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.

Using cmdstanpy with Windows requires a Unix-compatible C compiler such as mingw-gcc. If cmdstanpy is installed first, one can be installed via the cmdstanpy.install_cxx_toolchain command.

• Upgraded cmdstan version to 2.33.1, enabling Apple M2 support.
• Added pre-built wheels for macOS arm64 architecture (M1, M2 chips)
• Added argument scaling to the Prophet() instantiation. Allows minmax scaling on y instead of absmax scaling (dividing by the maximum value). scaling='absmax' by default, preserving the behaviour of previous versions.
• Added argument holidays_mode to the Prophet() instantiation. Allows holidays regressors to have a different mode than seasonality regressors. holidays_mode takes the same value as seasonality_mode if not specified, preserving the behaviour of previous versions.
• Added two methods to the Prophet object: preprocess() and calculate_initial_params(). These do not need to be called and will not change the model fitting process. Their purpose is to provide clarity on the pre-processing steps taken (y scaling, creating fourier series, regressor scaling, setting changepoints, etc.) before the data is passed to the stan model.
• Added argument extra_output_columns to cross_validation(). The user can specify additional columns from predict() to include in the final output alongside ds and yhat, for example extra_output_columns=['trend'].
• prophet's custom hdays module was deprecated last version and is now removed.

• Updated holidays data based on holidays version 0.34.

• We now rely solely on holidays package for country holidays.
• Upgraded cmdstan version to 2.31.0, enabling Apple M1 support.
• Fixed bug with Windows installation caused by long paths.

• Updated holidays data based on holidays version 0.25.

• Sped up .predict() by up to 10x by removing intermediate DataFrame creations.
• Sped up fourier series generation, leading to at least 1.5x speed improvement for train() and predict() pipelines.
• Fixed bug in how warm start values were being read.
• Wheels are now version-agnostic.

• Fixed a bug in construct_holiday_dataframe()
• Updated holidays data based on holidays version 0.18.

• (Python) Improved runtime (3-7x) of uncertainty predictions via vectorization.
• Bugfixes relating to Python package versions and R holiday objects.

• Replaced pystan2 dependency with cmdstan + cmdstanpy.
• Pre-packaged model binaries for Python package, uploaded binary distributions to PyPI.
• Improvements in the stan model code, cross-validation metric calculations, holidays.

• 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

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

• Fix bugs related to upstream changes in holidays and pandas packages.
• Compile model during first use, not during install (to comply with CRAN policy)
• cmdstanpy backend now available in Python
• Python 2 no longer supported

• Conditional seasonalities
• Improved cross validation estimates
• Plotly plot in Python
• Bugfixes

• Added holidays functionality
• Bugfixes

• 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

• Bugfixes

• Forecasting with sub-daily data
• Daily seasonality, and custom seasonalities
• Extra regressors
• Access to posterior predictive samples
• Cross-validation function
• Saturating minimums
• Bugfixes

• Bugfixes
• New options for detecting yearly and weekly seasonality (now the default)

• Initial release

Prophet is licensed under the MIT license.