Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
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.
Prophet is a CRAN package so you can use install.packages
.
install.packages('prophet')
After installation, you can get started!
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
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.
pystan2
dependency with cmdstan
+ cmdstanpy
.stan
model code, cross-validation metric calculations, holidays.holidays
and pandas
holidays
and pandas
packages.cmdstanpy
backend now available in PythonProphet is licensed under the MIT license.