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.
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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 -r requirements.txt
python setup.py develop
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 setup.py develop
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 pandasholidays and pandas packages.cmdstanpy backend now available in PythonProphet is licensed under the [MIT license](LICENSE).
*Note that all licence references and agreements mentioned in the Prophet README section above
are relevant to that project's source code only.
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