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|
%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.aliyun.com/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
* Thu Jun 08 2023 Python_Bot <Python_Bot@openeuler.org> - 0.20.5-1
- Package Spec generated
|
