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@@ -0,0 +1 @@ +/timemachines-0.20.5.tar.gz diff --git a/python-timemachines.spec b/python-timemachines.spec new file mode 100644 index 0000000..64d2bf0 --- /dev/null +++ b/python-timemachines.spec @@ -0,0 +1,314 @@ +%global _empty_manifest_terminate_build 0 +Name: python-timemachines +Version: 0.20.5 +Release: 1 +Summary: Evaluation and standardization of autonomous time series prediction +License: MIT +URL: https://github.com/microprediction/timemachines +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/dd/1b/ab418796cd69a1f7d185decc83aed7da41cd1b2e5f04185c954342e08f2f/timemachines-0.20.5.tar.gz +BuildArch: noarch + +Requires: python3-wheel +Requires: python3-numpy +Requires: python3-importlib-metadata +Requires: python3-microconventions +Requires: python3-pytz +Requires: python3-convertdate +Requires: python3-momentum +Requires: python3-requests +Requires: python3-scipy +Requires: python3-scikit-learn +Requires: python3-statsmodels +Requires: python3-pandas +Requires: python3-cython +Requires: python3-river +Requires: python3-pmdarima +Requires: python3-sktime +Requires: python3-statsforecast + +%description +# timemachines                  + +# Simple prediction functions ([documented](https://microprediction.github.io/timemachines/) and [assessed](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/overall.html)) +Because [why not](https://microprediction.github.io/timemachines/why) do things in **walk-forward incremental** fashion with **one line of code**? Here yt is a vector or scalar, and we want to predict yt (or its first coordinate if a vector) three steps in advance. + + from timemachines.skaters.somepackage.somevariety import something as f + for yt in y: + xt, xt_std, s = f(y=yt, s=s, k=3) + +This emits a k-vector xt of forecasts, and corresponding k-vector xt_std of estimated standard errors. See [skaters](https://microprediction.github.io/timemachines/skaters) for choices of somepackage, somevariety and something. You can also ensemble, compose, bootstrap and do other things with one line of code. + +See the [docs](https://microprediction.github.io/timemachines/). + +### Packages used + +Skaters draw on functionality from [popular python time-series packages](https://www.microprediction.com/blog/popular-timeseries-packages) like [river](https://github.com/online-ml/river), [pydlm](https://github.com/wwrechard/pydlm), [tbats](https://github.com/intive-DataScience/tbats), [pmdarima](http://alkaline-ml.com/pmdarima/), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html), [neuralprophet](https://neuralprophet.com/), Facebook [Prophet](https://facebook.github.io/prophet/), + Uber's [orbit](https://eng.uber.com/orbit/), Facebook's [greykite](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library) and more. See the [docs](https://microprediction.github.io/timemachines/). + +## What's a "skater"? +More abstractly: + +$$ + f : (y_t, state; k) \mapsto ( [\hat{y}(t+1),\hat{y}(t+2),\dots,\hat{y}(t+k) ], [\sigma(t+1),\dots,\sigma(t+k)], posterior\ state)) +$$ + +where $\sigma(t+l)$ estimates the standard error of the prediction $\hat{y}(t+l)$. + +If you prefer an legitimate (i.e. stateful) state machine, see [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md) question 1. + + +### Skater function conventions + +See [docs/interface](https://microprediction.github.io/timemachines/interface) for description of skater inputs and outputs. Briefly: + + x, w, s = f( y:Union[float,[float]], # Contemporaneously observerd data, + # ... including exogenous variables in y[1:], if any. + s=None, # Prior state + k:float=1, # Number of steps ahead to forecast. Typically integer. + a:[float]=None, # Variable(s) known in advance, or conditioning + t:float=None, # Time of observation (epoch seconds) + e:float=None, # Non-binding maximal computation time ("e for expiry"), in seconds + r:float=None) # Hyper-parameters ("r" stands for for hype(r)-pa(r)amete(r)s) + +### Contributions and capstone projects + +- See [CONTRIBUTE.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE.md) and [good first issues](https://github.com/microprediction/timemachines/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). +- See the suggested steps for a [capstone project](https://microprediction.github.io/timemachines/capstone.html). + +### Getting live help + +- [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md). +- See the Slack invite on my user page [here](https://github.com/microprediction/slack). +- Office hours [here](https://github.com/microprediction/meet). +- Learn how to deploy some of these models and try to win the [daily $125 prize](https://www.microprediction.com/competitions/daily). + +### Install [instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md) + +Oh what a mess the Python timeseries ecosystem is. So packages are not installed by default. See the methodical [install instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md) and be incremental for best results. The infamous [xkcd cartoon](https://xkcd.com/1987/) really does describe the alternative quite well. + + + + + +### Cite + +Thanks + + @electronic{cottontimemachines, + title = {{Timemachines: A Python Package for Creating and Assessing Autonomous Time-Series Prediction Algorithms}}, + year = {2021}, + author = {Peter Cotton}, + url = {https://github.com/microprediction/timemachines} + } + +or something [here](https://github.com/microprediction/microprediction/blob/master/CITE.md). + + +%package -n python3-timemachines +Summary: Evaluation and standardization of autonomous time series prediction +Provides: python-timemachines +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-timemachines +# timemachines                  + +# Simple prediction functions ([documented](https://microprediction.github.io/timemachines/) and [assessed](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/overall.html)) +Because [why not](https://microprediction.github.io/timemachines/why) do things in **walk-forward incremental** fashion with **one line of code**? Here yt is a vector or scalar, and we want to predict yt (or its first coordinate if a vector) three steps in advance. + + from timemachines.skaters.somepackage.somevariety import something as f + for yt in y: + xt, xt_std, s = f(y=yt, s=s, k=3) + +This emits a k-vector xt of forecasts, and corresponding k-vector xt_std of estimated standard errors. See [skaters](https://microprediction.github.io/timemachines/skaters) for choices of somepackage, somevariety and something. You can also ensemble, compose, bootstrap and do other things with one line of code. + +See the [docs](https://microprediction.github.io/timemachines/). + +### Packages used + +Skaters draw on functionality from [popular python time-series packages](https://www.microprediction.com/blog/popular-timeseries-packages) like [river](https://github.com/online-ml/river), [pydlm](https://github.com/wwrechard/pydlm), [tbats](https://github.com/intive-DataScience/tbats), [pmdarima](http://alkaline-ml.com/pmdarima/), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html), [neuralprophet](https://neuralprophet.com/), Facebook [Prophet](https://facebook.github.io/prophet/), + Uber's [orbit](https://eng.uber.com/orbit/), Facebook's [greykite](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library) and more. See the [docs](https://microprediction.github.io/timemachines/). + +## What's a "skater"? +More abstractly: + +$$ + f : (y_t, state; k) \mapsto ( [\hat{y}(t+1),\hat{y}(t+2),\dots,\hat{y}(t+k) ], [\sigma(t+1),\dots,\sigma(t+k)], posterior\ state)) +$$ + +where $\sigma(t+l)$ estimates the standard error of the prediction $\hat{y}(t+l)$. + +If you prefer an legitimate (i.e. stateful) state machine, see [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md) question 1. + + +### Skater function conventions + +See [docs/interface](https://microprediction.github.io/timemachines/interface) for description of skater inputs and outputs. Briefly: + + x, w, s = f( y:Union[float,[float]], # Contemporaneously observerd data, + # ... including exogenous variables in y[1:], if any. + s=None, # Prior state + k:float=1, # Number of steps ahead to forecast. Typically integer. + a:[float]=None, # Variable(s) known in advance, or conditioning + t:float=None, # Time of observation (epoch seconds) + e:float=None, # Non-binding maximal computation time ("e for expiry"), in seconds + r:float=None) # Hyper-parameters ("r" stands for for hype(r)-pa(r)amete(r)s) + +### Contributions and capstone projects + +- See [CONTRIBUTE.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE.md) and [good first issues](https://github.com/microprediction/timemachines/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). +- See the suggested steps for a [capstone project](https://microprediction.github.io/timemachines/capstone.html). + +### Getting live help + +- [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md). +- See the Slack invite on my user page [here](https://github.com/microprediction/slack). +- Office hours [here](https://github.com/microprediction/meet). +- Learn how to deploy some of these models and try to win the [daily $125 prize](https://www.microprediction.com/competitions/daily). + +### Install [instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md) + +Oh what a mess the Python timeseries ecosystem is. So packages are not installed by default. See the methodical [install instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md) and be incremental for best results. The infamous [xkcd cartoon](https://xkcd.com/1987/) really does describe the alternative quite well. + + + + + +### Cite + +Thanks + + @electronic{cottontimemachines, + title = {{Timemachines: A Python Package for Creating and Assessing Autonomous Time-Series Prediction Algorithms}}, + year = {2021}, + author = {Peter Cotton}, + url = {https://github.com/microprediction/timemachines} + } + +or something [here](https://github.com/microprediction/microprediction/blob/master/CITE.md). + + +%package help +Summary: Development documents and examples for timemachines +Provides: python3-timemachines-doc +%description help +# timemachines                  + +# Simple prediction functions ([documented](https://microprediction.github.io/timemachines/) and [assessed](https://microprediction.github.io/timeseries-elo-ratings/html_leaderboards/overall.html)) +Because [why not](https://microprediction.github.io/timemachines/why) do things in **walk-forward incremental** fashion with **one line of code**? Here yt is a vector or scalar, and we want to predict yt (or its first coordinate if a vector) three steps in advance. + + from timemachines.skaters.somepackage.somevariety import something as f + for yt in y: + xt, xt_std, s = f(y=yt, s=s, k=3) + +This emits a k-vector xt of forecasts, and corresponding k-vector xt_std of estimated standard errors. See [skaters](https://microprediction.github.io/timemachines/skaters) for choices of somepackage, somevariety and something. You can also ensemble, compose, bootstrap and do other things with one line of code. + +See the [docs](https://microprediction.github.io/timemachines/). + +### Packages used + +Skaters draw on functionality from [popular python time-series packages](https://www.microprediction.com/blog/popular-timeseries-packages) like [river](https://github.com/online-ml/river), [pydlm](https://github.com/wwrechard/pydlm), [tbats](https://github.com/intive-DataScience/tbats), [pmdarima](http://alkaline-ml.com/pmdarima/), [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html), [neuralprophet](https://neuralprophet.com/), Facebook [Prophet](https://facebook.github.io/prophet/), + Uber's [orbit](https://eng.uber.com/orbit/), Facebook's [greykite](https://engineering.linkedin.com/blog/2021/greykite--a-flexible--intuitive--and-fast-forecasting-library) and more. See the [docs](https://microprediction.github.io/timemachines/). + +## What's a "skater"? +More abstractly: + +$$ + f : (y_t, state; k) \mapsto ( [\hat{y}(t+1),\hat{y}(t+2),\dots,\hat{y}(t+k) ], [\sigma(t+1),\dots,\sigma(t+k)], posterior\ state)) +$$ + +where $\sigma(t+l)$ estimates the standard error of the prediction $\hat{y}(t+l)$. + +If you prefer an legitimate (i.e. stateful) state machine, see [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md) question 1. + + +### Skater function conventions + +See [docs/interface](https://microprediction.github.io/timemachines/interface) for description of skater inputs and outputs. Briefly: + + x, w, s = f( y:Union[float,[float]], # Contemporaneously observerd data, + # ... including exogenous variables in y[1:], if any. + s=None, # Prior state + k:float=1, # Number of steps ahead to forecast. Typically integer. + a:[float]=None, # Variable(s) known in advance, or conditioning + t:float=None, # Time of observation (epoch seconds) + e:float=None, # Non-binding maximal computation time ("e for expiry"), in seconds + r:float=None) # Hyper-parameters ("r" stands for for hype(r)-pa(r)amete(r)s) + +### Contributions and capstone projects + +- See [CONTRIBUTE.md](https://github.com/microprediction/timemachines/blob/main/CONTRIBUTE.md) and [good first issues](https://github.com/microprediction/timemachines/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22). +- See the suggested steps for a [capstone project](https://microprediction.github.io/timemachines/capstone.html). + +### Getting live help + +- [FAQ](https://github.com/microprediction/timemachines/blob/main/FAQ.md). +- See the Slack invite on my user page [here](https://github.com/microprediction/slack). +- Office hours [here](https://github.com/microprediction/meet). +- Learn how to deploy some of these models and try to win the [daily $125 prize](https://www.microprediction.com/competitions/daily). + +### Install [instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md) + +Oh what a mess the Python timeseries ecosystem is. So packages are not installed by default. See the methodical [install instructions](https://github.com/microprediction/timemachines/blob/main/INSTALL.md) and be incremental for best results. The infamous [xkcd cartoon](https://xkcd.com/1987/) really does describe the alternative quite well. + + + + + +### Cite + +Thanks + + @electronic{cottontimemachines, + title = {{Timemachines: A Python Package for Creating and Assessing Autonomous Time-Series Prediction Algorithms}}, + year = {2021}, + author = {Peter Cotton}, + url = {https://github.com/microprediction/timemachines} + } + +or something [here](https://github.com/microprediction/microprediction/blob/master/CITE.md). + + +%prep +%autosetup -n timemachines-0.20.5 + +%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-timemachines -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Mon May 15 2023 Python_Bot <Python_Bot@openeuler.org> - 0.20.5-1 +- Package Spec generated @@ -0,0 +1 @@ +3381c8d99e5e39932835d0e163423891 timemachines-0.20.5.tar.gz |