Package: prophet-nogpl
Prophet-NoGPL: Automatic Forecasting Procedure without GPL DependencyThis is a fork of the Prophet. Its aim is to completely remove PyStan2 dependency which is licensed under GPL.How it works?PyStan2 is completely removed from the dependency and CmdStanPy is used instead. The preuilt stan model binary and dependenty shared library files are downloaded to the local directory at the first run. The direcory is set to$HOME/.prophet/by default and can be configured by thePROPHET_MODEL_DIR_PATHenv var.  (seeCmdStanPyBackendclass in themodels.py)Currently, only linux x86_64 binary is prebuilt and downloaded. If you are using other platform, you need to compile the model file and dependent shared library (libtbb.so.2) bycomple_cmdstanpy_model.pyand move them to thePROPHET_MODEL_DIR_PATH. To compile the model, you need to install CmdStan, CmdStanPy and C++ compiler. The prebuilt CmdStan can be downloaded fromhttps://github.com/stan-dev/cmdstan/releases. After downloading one that is compatible to your system, untar and put it under$HOME/.cmdstan. The directory structure should be like this.$HOME/
    .cmdstan/
        cmdstan-<version>/
            bin/Then make sure there is astancexecutable in thebindirectory. If there is only something likelinux-stanc,mac-stancetc, rename it tostancand append exec permission.Below is the original Readme content. Some of them can be inconsistent to this package.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 isopen source softwarereleased by Facebook'sCore Data Science team. It is available for download onCRANandPyPI.Important linksHomepage:https://facebook.github.io/prophet/HTML documentation:https://facebook.github.io/prophet/docs/quick_start.htmlIssue tracker:https://github.com/facebook/prophet/issuesSource code repository:https://github.com/facebook/prophetContributing:https://facebook.github.io/prophet/docs/contributing.htmlProphet R package:https://cran.r-project.org/package=prophetProphet Python package:https://pypi.python.org/pypi/prophet/Release blogpost:https://research.fb.com/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).Installation in RProphet is aCRAN packageso you can useinstall.packages.install.packages('prophet')After installation, you canget started!Experimental backend - cmdstanrYou can also choose an experimental alternative stan backend calledcmdstanr. Once you've installedprophet,
follow these instructions to usecmdstanrinstead ofrstanas the backend:# R# We recommend running this in a fresh R session or restarting your current sessioninstall.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=<yourexistingcmdstan>)# Set the R_STAN_BACKEND environment variableSys.setenv(R_STAN_BACKEND="CMDSTANR")WindowsOn Windows, R requires a compiler so you'll need tofollow the instructionsprovided byrstan. The key step is installingRtoolsbefore attempting to install the package.If you have custom Stan compiler settings, install from source rather than the CRAN binary.Installation in PythonProphet is on PyPI, so you can usepipto 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 supportedpipinstallpystan==2.19.1.1

pipinstallprophetThe default dependency that Prophet has ispystan. PyStan has its owninstallation instructions. Install pystan with pip before using pip to install prophet.Experimental backend - cmdstanpyYou can also choose a (more experimental) alternative stan backend calledcmdstanpy. It requires theCmdStancommand line interface and you will have to specify the environment variableSTAN_BACKENDpointing to it, for example:# bash$CMDSTAN=/tmp/cmdstan-2.22.1STAN_BACKEND=CMDSTANPYpipinstallprophetNote that theCMDSTANvariable is directly related tocmdstanpymodule and can be omitted if your CmdStan binaries are in your$PATH.It is also possible to install Prophet with two backends:# bash$CMDSTAN=/tmp/cmdstan-2.22.1STAN_BACKEND=PYSTAN,CMDSTANPYpipinstallprophetAfter installation, you canget started!If you upgrade the version of PyStan installed on your system, you may need to reinstall prophet (see here).AnacondaUseconda install gccto set up gcc. The easiest way to install Prophet is through conda-forge:conda install -c conda-forge prophet.WindowsOn Windows, PyStan requires a compiler so you'll need tofollow the instructions. The easiest way to install Prophet in Windows is in Anaconda.LinuxMake 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.ChangelogVersion 1.0 (2021.03.28)Python package name changed from fbprophet to prophetFixed R Windows build issues to get latest version back on CRANImprovements in serialization, holidays, and R timezone handlingPlotting improvementsVersion 0.7 (2020.09.05)Built-in json serializationAdded "flat" growth optionBugfixes related toholidaysandpandasPlotting improvementsImprovements in cross validation, such as parallelization and directly specifying cutoffsVersion 0.6 (2020.03.03)Fix bugs related to upstream changes inholidaysandpandaspackages.Compile model during first use, not during install (to comply with CRAN policy)cmdstanpybackend now available in PythonPython 2 no longer supportedVersion 0.5 (2019.05.14)Conditional seasonalitiesImproved cross validation estimatesPlotly plot in PythonBugfixesVersion 0.4 (2018.12.18)Added holidays functionalityBugfixesVersion 0.3 (2018.06.01)Multiplicative seasonalityCross validation error metrics and visualizationsParameter to set range of potential changepointsUnified Stan model for both trend typesImproved future trend uncertainty for sub-daily dataBugfixesVersion 0.2.1 (2017.11.08)BugfixesVersion 0.2 (2017.09.02)Forecasting with sub-daily dataDaily seasonality, and custom seasonalitiesExtra regressorsAccess to posterior predictive samplesCross-validation functionSaturating minimumsBugfixesVersion 0.1.1 (2017.04.17)BugfixesNew options for detecting yearly and weekly seasonality (now the default)Version 0.1 (2017.02.23)Initial releaseLicenseProphet is licensed under theMIT license.