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%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 <Python_Bot@openeuler.org> - 1.1.2-1
- Package Spec generated
|
