%global _empty_manifest_terminate_build 0 Name: python-prophet Version: 1.1.2 Release: 1 Summary: Automatic Forecasting Procedure License: MIT URL: https://pypi.org/project/prophet/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/2d/d0/9de2137166e014133bb0df613c4eab9988ff3f547ac61c91fd584221be68/prophet-1.1.2.tar.gz Requires: python3-cmdstanpy Requires: python3-numpy Requires: python3-matplotlib Requires: python3-pandas Requires: python3-LunarCalendar Requires: python3-convertdate Requires: python3-holidays Requires: python3-dateutil Requires: python3-tqdm Requires: python3-setuptools Requires: python3-wheel Requires: python3-pytest Requires: python3-jupyterlab Requires: python3-nbconvert Requires: python3-plotly Requires: python3-dask[dataframe] Requires: python3-distributed %description # Prophet: Automatic Forecasting Procedure 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](https://code.facebook.com/projects/>) released by [Facebook's Core Data Science team ](https://research.fb.com/category/data-science/). Full documentation and examples available at the homepage: https://facebook.github.io/prophet/ ## Important links - 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 - Implementation of Prophet in R: https://cran.r-project.org/package=prophet ## Other forecasting packages - Rob Hyndman's [forecast package](http://robjhyndman.com/software/forecast/) - [Statsmodels](http://statsmodels.sourceforge.net/) ## Installation - PyPI release See [Installation in Python - PyPI release](https://github.com/facebook/prophet#installation-in-python---pypi-release) ## Installation - Development version See [Installation in Python - Development version](https://github.com/facebook/prophet#installation-in-python---development-version) ### Installation using Docker and docker-compose (via Makefile) Simply type `make build` and if everything is fine you should be able to `make shell` or alternative jump directly to `make py-shell`. To run the tests, inside the container `cd python/prophet` and then `python -m pytest prophet/tests/` ### Example usage ```python >>> from prophet import Prophet >>> m = Prophet() >>> m.fit(df) # df is a pandas.DataFrame with 'y' and 'ds' columns >>> future = m.make_future_dataframe(periods=365) >>> m.predict(future) ``` %package -n python3-prophet Summary: Automatic Forecasting Procedure Provides: python-prophet BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip BuildRequires: python3-cffi BuildRequires: gcc BuildRequires: gdb %description -n python3-prophet # Prophet: Automatic Forecasting Procedure 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](https://code.facebook.com/projects/>) released by [Facebook's Core Data Science team ](https://research.fb.com/category/data-science/). Full documentation and examples available at the homepage: https://facebook.github.io/prophet/ ## Important links - 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 - Implementation of Prophet in R: https://cran.r-project.org/package=prophet ## Other forecasting packages - Rob Hyndman's [forecast package](http://robjhyndman.com/software/forecast/) - [Statsmodels](http://statsmodels.sourceforge.net/) ## Installation - PyPI release See [Installation in Python - PyPI release](https://github.com/facebook/prophet#installation-in-python---pypi-release) ## Installation - Development version See [Installation in Python - Development version](https://github.com/facebook/prophet#installation-in-python---development-version) ### Installation using Docker and docker-compose (via Makefile) Simply type `make build` and if everything is fine you should be able to `make shell` or alternative jump directly to `make py-shell`. To run the tests, inside the container `cd python/prophet` and then `python -m pytest prophet/tests/` ### Example usage ```python >>> from prophet import Prophet >>> m = Prophet() >>> m.fit(df) # df is a pandas.DataFrame with 'y' and 'ds' columns >>> future = m.make_future_dataframe(periods=365) >>> m.predict(future) ``` %package help Summary: Development documents and examples for prophet Provides: python3-prophet-doc %description help # Prophet: Automatic Forecasting Procedure 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](https://code.facebook.com/projects/>) released by [Facebook's Core Data Science team ](https://research.fb.com/category/data-science/). Full documentation and examples available at the homepage: https://facebook.github.io/prophet/ ## Important links - 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 - Implementation of Prophet in R: https://cran.r-project.org/package=prophet ## Other forecasting packages - Rob Hyndman's [forecast package](http://robjhyndman.com/software/forecast/) - [Statsmodels](http://statsmodels.sourceforge.net/) ## Installation - PyPI release See [Installation in Python - PyPI release](https://github.com/facebook/prophet#installation-in-python---pypi-release) ## Installation - Development version See [Installation in Python - Development version](https://github.com/facebook/prophet#installation-in-python---development-version) ### Installation using Docker and docker-compose (via Makefile) Simply type `make build` and if everything is fine you should be able to `make shell` or alternative jump directly to `make py-shell`. To run the tests, inside the container `cd python/prophet` and then `python -m pytest prophet/tests/` ### Example usage ```python >>> from prophet import Prophet >>> m = Prophet() >>> m.fit(df) # df is a pandas.DataFrame with 'y' and 'ds' columns >>> future = m.make_future_dataframe(periods=365) >>> m.predict(future) ``` %prep %autosetup -n prophet-1.1.2 %build %py3_build %install %py3_install install -d -m755 %{buildroot}/%{_pkgdocdir} if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi pushd %{buildroot} if [ -d usr/lib ]; then find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/lib64 ]; then find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/bin ]; then find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst fi if [ -d usr/sbin ]; then find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst fi touch doclist.lst if [ -d usr/share/man ]; then find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst fi popd mv %{buildroot}/filelist.lst . mv %{buildroot}/doclist.lst . %files -n python3-prophet -f filelist.lst %dir %{python3_sitearch}/* %files help -f doclist.lst %{_docdir}/* %changelog * Mon Apr 10 2023 Python_Bot - 1.1.2-1 - Package Spec generated