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%global _empty_manifest_terminate_build 0
Name: python-ChangePointDetectorEVT
Version: 0.0.18
Release: 1
Summary: This module takes a date time series and returns: (a) the underlaying linear trend and (b) the times where there is a change in the trend
License: MIT License
URL: https://github.com/mhaupt63/ChangePointDetector
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/1a/fd/d1cbdda9b01a4cdc1f69df8936add62f6792076171ec8e7174e9fe77fd80/ChangePointDetectorEVT-0.0.18.tar.gz
BuildArch: noarch
%description
ChangePointDetector
This module returns change points in a date time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf
1. from ChangePointDetector import ChangePointDetector
2. Prepare your time series as data plus Panda dates
3. Create the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.:
Session=ChangePointDetector.ChangePointDetectorSession(data,dates).
- 'SeasonalityPeriods' is an optional input, e.g if your data is sequential months, 12 = calendar month seasonality
- 'ForecastPeriods' is another optional input, indicating how many periods to forecast. Default = 3
4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction()
This will return a "Results" object that contains the following:
- ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints
- Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 dates
- PredictionVariance. Variance of the smoothed actuals and forecast
- ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made)
- Trend. This is the linear change factor
- TrendVariance. Variance of the trend
%package -n python3-ChangePointDetectorEVT
Summary: This module takes a date time series and returns: (a) the underlaying linear trend and (b) the times where there is a change in the trend
Provides: python-ChangePointDetectorEVT
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-ChangePointDetectorEVT
ChangePointDetector
This module returns change points in a date time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf
1. from ChangePointDetector import ChangePointDetector
2. Prepare your time series as data plus Panda dates
3. Create the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.:
Session=ChangePointDetector.ChangePointDetectorSession(data,dates).
- 'SeasonalityPeriods' is an optional input, e.g if your data is sequential months, 12 = calendar month seasonality
- 'ForecastPeriods' is another optional input, indicating how many periods to forecast. Default = 3
4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction()
This will return a "Results" object that contains the following:
- ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints
- Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 dates
- PredictionVariance. Variance of the smoothed actuals and forecast
- ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made)
- Trend. This is the linear change factor
- TrendVariance. Variance of the trend
%package help
Summary: Development documents and examples for ChangePointDetectorEVT
Provides: python3-ChangePointDetectorEVT-doc
%description help
ChangePointDetector
This module returns change points in a date time series, using Kalman filters and EVT as described in https://www.robots.ox.ac.uk/~sjrob/Pubs/LeeRoberts_EVT.pdf
1. from ChangePointDetector import ChangePointDetector
2. Prepare your time series as data plus Panda dates
3. Create the necessary Kalman representation by creating a "session" object by calling the ChangePoint class, e.g.:
Session=ChangePointDetector.ChangePointDetectorSession(data,dates).
- 'SeasonalityPeriods' is an optional input, e.g if your data is sequential months, 12 = calendar month seasonality
- 'ForecastPeriods' is another optional input, indicating how many periods to forecast. Default = 3
4. Determine the changepoints by running the ChangePointDetectorFunction on your "session", e.g. Results=Session.ChangePointDetectorFunction()
This will return a "Results" object that contains the following:
- ChangePoints. This is a list of 0s and 1s the length of the data, where 1s represent changepoints
- Prediction. This is the Kalman smoothed actuals, plus a 3 period forecast. Note no forecast will be made if there is a changepoint in the last 3 dates
- PredictionVariance. Variance of the smoothed actuals and forecast
- ExtendedDates. These are the original dates plus 3 exta for the forecast (if a forecast has been made)
- Trend. This is the linear change factor
- TrendVariance. Variance of the trend
%prep
%autosetup -n ChangePointDetectorEVT-0.0.18
%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-ChangePointDetectorEVT -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.18-1
- Package Spec generated
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