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|
%global _empty_manifest_terminate_build 0
Name: python-fbprophet
Version: 0.7.1
Release: 1
Summary: Automatic Forecasting Procedure
License: MIT
URL: https://facebook.github.io/prophet/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1a/b5/9c3fefa8a7b839729df57deedf0a69815841dfb88f0df911f34d998230b7/fbprophet-0.7.1.tar.gz
BuildArch: noarch
%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
```shell
pip install fbprophet
```
Note: Installation requires PyStan, which has its [own installation instructions](http://pystan.readthedocs.io/en/latest/installation_beginner.html).
On Windows, PyStan requires a compiler so you'll need to [follow the instructions](http://pystan.readthedocs.io/en/latest/windows.html).
The key step is installing a recent [C++ compiler](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
## 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/fbprophet` and then `python -m unittest`
### Example usage
```python
>>> from fbprophet 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-fbprophet
Summary: Automatic Forecasting Procedure
Provides: python-fbprophet
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-fbprophet
# 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
```shell
pip install fbprophet
```
Note: Installation requires PyStan, which has its [own installation instructions](http://pystan.readthedocs.io/en/latest/installation_beginner.html).
On Windows, PyStan requires a compiler so you'll need to [follow the instructions](http://pystan.readthedocs.io/en/latest/windows.html).
The key step is installing a recent [C++ compiler](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
## 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/fbprophet` and then `python -m unittest`
### Example usage
```python
>>> from fbprophet 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 fbprophet
Provides: python3-fbprophet-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
```shell
pip install fbprophet
```
Note: Installation requires PyStan, which has its [own installation instructions](http://pystan.readthedocs.io/en/latest/installation_beginner.html).
On Windows, PyStan requires a compiler so you'll need to [follow the instructions](http://pystan.readthedocs.io/en/latest/windows.html).
The key step is installing a recent [C++ compiler](https://visualstudio.microsoft.com/visual-cpp-build-tools/)
## 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/fbprophet` and then `python -m unittest`
### Example usage
```python
>>> from fbprophet 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 fbprophet-0.7.1
%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-fbprophet -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri Apr 21 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.1-1
- Package Spec generated
|
