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Apr. 20, 2018
Nov. 16, 2016

Prophet: Automatic Forecasting Procedure

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.

Important links

Installation in R

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

# R
> install.packages('prophet')

After installation, you can get started!


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.

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.

After installation, you can get started!


On Windows, PyStan requires a compiler so you'll need to follow the instructions. The key step is installing a recent C++ compiler.


Make sure compilers (gcc, g++, build-essential) and Python development tools (python-dev, python3-dev) are installed. 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.


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


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

Latest Releases
 Nov. 9 2017
 Sep. 12 2017
 Sep. 12 2017
 Apr. 17 2017
 Apr. 17 2017