%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 * Tue May 30 2023 Python_Bot - 0.0.18-1 - Package Spec generated