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%global _empty_manifest_terminate_build 0
Name: python-stldecompose
Version: 0.0.5
Release: 1
Summary: A Python implementation of seasonal trend with Loess (STL) time series decomposition
License: MIT
URL: https://github.com/jrmontag/STLDecompose
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/36/78/04d96ebbacf2c8d0876167c900e248f10440a84cd2e59490044a2944b84e/stldecompose-0.0.5.tar.gz
BuildArch: noarch
Requires: python3-pandas
Requires: python3-numpy
Requires: python3-scipy
Requires: python3-statsmodels
Requires: python3-matplotlib
%description
This is a relatively naive Python implementation of a seasonal and trend decomposition using Loess smoothing. Commonly referred to as an "STL decomposition", Cleveland's 1990 paper is the canonical reference.
This implementation is a variation of (and takes inspiration from) the implementation of the ``seasonal_decompose`` method `in statsmodels <http://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html#statsmodels.tsa.seasonal.seasonal_decompose>`_. In this implementation, the trend component is calculated by substituting a configurable `Loess regression <https://en.wikipedia.org/wiki/Local_regression>`_ for the convolutional method used in ``seasonal_decompose``. It also extends the existing ``DecomposeResult`` from ``statsmodels`` to allow for forecasting based on the calculated decomposition.
%package -n python3-stldecompose
Summary: A Python implementation of seasonal trend with Loess (STL) time series decomposition
Provides: python-stldecompose
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-stldecompose
This is a relatively naive Python implementation of a seasonal and trend decomposition using Loess smoothing. Commonly referred to as an "STL decomposition", Cleveland's 1990 paper is the canonical reference.
This implementation is a variation of (and takes inspiration from) the implementation of the ``seasonal_decompose`` method `in statsmodels <http://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html#statsmodels.tsa.seasonal.seasonal_decompose>`_. In this implementation, the trend component is calculated by substituting a configurable `Loess regression <https://en.wikipedia.org/wiki/Local_regression>`_ for the convolutional method used in ``seasonal_decompose``. It also extends the existing ``DecomposeResult`` from ``statsmodels`` to allow for forecasting based on the calculated decomposition.
%package help
Summary: Development documents and examples for stldecompose
Provides: python3-stldecompose-doc
%description help
This is a relatively naive Python implementation of a seasonal and trend decomposition using Loess smoothing. Commonly referred to as an "STL decomposition", Cleveland's 1990 paper is the canonical reference.
This implementation is a variation of (and takes inspiration from) the implementation of the ``seasonal_decompose`` method `in statsmodels <http://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html#statsmodels.tsa.seasonal.seasonal_decompose>`_. In this implementation, the trend component is calculated by substituting a configurable `Loess regression <https://en.wikipedia.org/wiki/Local_regression>`_ for the convolutional method used in ``seasonal_decompose``. It also extends the existing ``DecomposeResult`` from ``statsmodels`` to allow for forecasting based on the calculated decomposition.
%prep
%autosetup -n stldecompose-0.0.5
%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-stldecompose -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.5-1
- Package Spec generated
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