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|
%global _empty_manifest_terminate_build 0
Name: python-orbit-ml
Version: 1.1.4.2
Release: 1
Summary: Orbit is a package for Bayesian time series modeling and inference.
License: Apache License 2.0
URL: https://orbit-ml.readthedocs.io/en/stable/
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/14/60/7ca25baa64042fa2ca90df4f4769d05f89dd9120b5497d34c1e5ae329d72/orbit-ml-1.1.4.2.tar.gz
BuildArch: noarch
%description
<!--- BADGES: START --->
[][#pypi-package]
[](https://github.com/uber/orbit/actions)
[](https://orbit-ml.readthedocs.io/en/latest/?badge=latest)
[][#pypi-package]
[](https://pepy.tech/project/orbit-ml)
[][#conda-forge-feedstock]
[][#conda-forge-package]
[][#conda-forge-package]
[][#github-license]
[#github-license]: https://github.com/uber/orbit/blob/master/LICENSE
[#pypi-package]: https://pypi.org/project/orbit-ml/
[#conda-forge-package]: https://anaconda.org/conda-forge/orbit-ml
[#conda-forge-feedstock]: https://github.com/conda-forge/orbit-ml-feedstock
<!--- BADGES: END --->
# User Notice
The default page of the repo is on `dev` branch. To install the dev version, please check the section `Installing from Dev Branch`. If you are looking for a **stable version**, please refer to the `master` branch [here](https://github.com/uber/orbit/tree/master).
# Disclaimer
This project
- is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
- requires PyStan as a system dependency. PyStan is licensed under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html), which is a free, copyleft license for software.
# Orbit: A Python Package for Bayesian Forecasting
Orbit is a Python package for Bayesian time series forecasting and inference. It provides a
familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.
For details, check out our documentation and tutorials:
- HTML (stable): https://orbit-ml.readthedocs.io/en/stable/
- HTML (latest): https://orbit-ml.readthedocs.io/en/latest/
Currently, it supports concrete implementations for the following models:
- Exponential Smoothing (ETS)
- Local Global Trend (LGT)
- Damped Local Trend (DLT)
- Kernel Time-based Regression (KTR)
It also supports the following sampling/optimization methods for model estimation/inferences:
- Markov-Chain Monte Carlo (MCMC) as a full sampling method
- Maximum a Posteriori (MAP) as a point estimate method
- Variational Inference (VI) as a hybrid-sampling method on approximate
distribution
## Installation
### Installing Stable Release
Install the library either from PyPi or from the source with `pip`.
Alternatively, you can also install it from Anaconda with `conda`:
**With pip**
1. Installing from PyPI
```sh
$ pip install orbit-ml
```
2. Install from source
```sh
$ git clone https://github.com/uber/orbit.git
$ cd orbit
$ pip install -r requirements.txt
$ pip install .
```
**With conda**
The library can be installed from the conda-forge channel using conda.
```sh
$ conda install -c conda-forge orbit-ml
```
### Installing from Dev Branch
```sh
$ pip install git+https://github.com/uber/orbit.git@dev
```
## Quick Start with Damped-Local-Trend (DLT) Model
### FULL Bayesian Prediction
```python
from orbit.utils.dataset import load_iclaims
from orbit.models import DLT
from orbit.diagnostics.plot import plot_predicted_data
# log-transformed data
df = load_iclaims()
# train-test split
test_size = 52
train_df = df[:-test_size]
test_df = df[-test_size:]
dlt = DLT(
response_col='claims', date_col='week',
regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
seasonality=52,
)
dlt.fit(df=train_df)
# outcomes data frame
predicted_df = dlt.predict(df=test_df)
plot_predicted_data(
training_actual_df=train_df, predicted_df=predicted_df,
date_col=dlt.date_col, actual_col=dlt.response_col,
test_actual_df=test_df
)
```
## Demo
Nowcasting with Regression in DLT:
[](https://colab.research.google.com/github/edwinnglabs/ts-playground/blob/master/Orbit_Tutorial.ipynb)
Backtest on M3 Data:
[](https://colab.research.google.com/github/edwinnglabs/ts-playground/blob/master/orbit_m3_backtest.ipynb)
More examples can be found under [tutorials](./docs/tutorials)
and [examples](./examples).
# Contributing
We welcome community contributors to the project. Before you start, please read our
[code of conduct](CODE_OF_CONDUCT.md) and check out
[contributing guidelines](CONTRIBUTING.md) first.
# Versioning
We document versions and changes in our [changelog](./docs/changelog.rst).
# References
## Presentations
Check out the ongoing [deck](https://docs.google.com/presentation/d/1WfTtXAW3rud4TX9HtB3NkE6buDE8tWk6BKZ2hRNXjCI/edit?usp=sharing) for scope and roadmap of the project. An older deck used in the [meet-up](https://www.meetup.com/UberEvents/events/279446143/) during July 2021 can also be found [here](https://docs.google.com/presentation/d/1R0Ol8xahIE6XlrAjAi0ewu4nRxo-wQn8w6U7z-uiOzI/edit?usp=sharing).
## Citation
To cite Orbit in publications, refer to the following whitepaper:
[Orbit: Probabilistic Forecast with Exponential Smoothing](https://arxiv.org/abs/2004.08492)
Bibtex:
```
@misc{
ng2020orbit,
title={Orbit: Probabilistic Forecast with Exponential Smoothing},
author={Edwin Ng,
Zhishi Wang,
Huigang Chen,
Steve Yang,
Slawek Smyl},
year={2020}, eprint={2004.08492}, archivePrefix={arXiv}, primaryClass={stat.CO}
}
```
## Papers
- Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip,
P., Horsfall, P., and Goodman, N. D. Pyro: Deep universal probabilistic programming. The Journal of Machine Learning
Research, 20(1):973–978, 2019.
- Hoffman, M.D. and Gelman, A. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.
J. Mach. Learn. Res., 15(1), pp.1593-1623, 2014.
- Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. Forecasting with exponential smoothing:
the state space approach. Springer Science & Business Media, 2008.
- Smyl, S. Zhang, Q. Fitting and Extending Exponential Smoothing Models with Stan.
International Symposium on Forecasting, 2015.
## Related projects
- [Pyro](https://github.com/pyro-ppl/pyro)
- [Stan](https://github.com/stan-dev/stan)
- [Rlgt](https://cran.r-project.org/web/packages/Rlgt/index.html)
- [forecast](https://github.com/robjhyndman/forecast)
- [prophet](https://facebook.github.io/prophet/)
%package -n python3-orbit-ml
Summary: Orbit is a package for Bayesian time series modeling and inference.
Provides: python-orbit-ml
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-orbit-ml
<!--- BADGES: START --->
[][#pypi-package]
[](https://github.com/uber/orbit/actions)
[](https://orbit-ml.readthedocs.io/en/latest/?badge=latest)
[][#pypi-package]
[](https://pepy.tech/project/orbit-ml)
[][#conda-forge-feedstock]
[][#conda-forge-package]
[][#conda-forge-package]
[][#github-license]
[#github-license]: https://github.com/uber/orbit/blob/master/LICENSE
[#pypi-package]: https://pypi.org/project/orbit-ml/
[#conda-forge-package]: https://anaconda.org/conda-forge/orbit-ml
[#conda-forge-feedstock]: https://github.com/conda-forge/orbit-ml-feedstock
<!--- BADGES: END --->
# User Notice
The default page of the repo is on `dev` branch. To install the dev version, please check the section `Installing from Dev Branch`. If you are looking for a **stable version**, please refer to the `master` branch [here](https://github.com/uber/orbit/tree/master).
# Disclaimer
This project
- is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
- requires PyStan as a system dependency. PyStan is licensed under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html), which is a free, copyleft license for software.
# Orbit: A Python Package for Bayesian Forecasting
Orbit is a Python package for Bayesian time series forecasting and inference. It provides a
familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.
For details, check out our documentation and tutorials:
- HTML (stable): https://orbit-ml.readthedocs.io/en/stable/
- HTML (latest): https://orbit-ml.readthedocs.io/en/latest/
Currently, it supports concrete implementations for the following models:
- Exponential Smoothing (ETS)
- Local Global Trend (LGT)
- Damped Local Trend (DLT)
- Kernel Time-based Regression (KTR)
It also supports the following sampling/optimization methods for model estimation/inferences:
- Markov-Chain Monte Carlo (MCMC) as a full sampling method
- Maximum a Posteriori (MAP) as a point estimate method
- Variational Inference (VI) as a hybrid-sampling method on approximate
distribution
## Installation
### Installing Stable Release
Install the library either from PyPi or from the source with `pip`.
Alternatively, you can also install it from Anaconda with `conda`:
**With pip**
1. Installing from PyPI
```sh
$ pip install orbit-ml
```
2. Install from source
```sh
$ git clone https://github.com/uber/orbit.git
$ cd orbit
$ pip install -r requirements.txt
$ pip install .
```
**With conda**
The library can be installed from the conda-forge channel using conda.
```sh
$ conda install -c conda-forge orbit-ml
```
### Installing from Dev Branch
```sh
$ pip install git+https://github.com/uber/orbit.git@dev
```
## Quick Start with Damped-Local-Trend (DLT) Model
### FULL Bayesian Prediction
```python
from orbit.utils.dataset import load_iclaims
from orbit.models import DLT
from orbit.diagnostics.plot import plot_predicted_data
# log-transformed data
df = load_iclaims()
# train-test split
test_size = 52
train_df = df[:-test_size]
test_df = df[-test_size:]
dlt = DLT(
response_col='claims', date_col='week',
regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
seasonality=52,
)
dlt.fit(df=train_df)
# outcomes data frame
predicted_df = dlt.predict(df=test_df)
plot_predicted_data(
training_actual_df=train_df, predicted_df=predicted_df,
date_col=dlt.date_col, actual_col=dlt.response_col,
test_actual_df=test_df
)
```
## Demo
Nowcasting with Regression in DLT:
[](https://colab.research.google.com/github/edwinnglabs/ts-playground/blob/master/Orbit_Tutorial.ipynb)
Backtest on M3 Data:
[](https://colab.research.google.com/github/edwinnglabs/ts-playground/blob/master/orbit_m3_backtest.ipynb)
More examples can be found under [tutorials](./docs/tutorials)
and [examples](./examples).
# Contributing
We welcome community contributors to the project. Before you start, please read our
[code of conduct](CODE_OF_CONDUCT.md) and check out
[contributing guidelines](CONTRIBUTING.md) first.
# Versioning
We document versions and changes in our [changelog](./docs/changelog.rst).
# References
## Presentations
Check out the ongoing [deck](https://docs.google.com/presentation/d/1WfTtXAW3rud4TX9HtB3NkE6buDE8tWk6BKZ2hRNXjCI/edit?usp=sharing) for scope and roadmap of the project. An older deck used in the [meet-up](https://www.meetup.com/UberEvents/events/279446143/) during July 2021 can also be found [here](https://docs.google.com/presentation/d/1R0Ol8xahIE6XlrAjAi0ewu4nRxo-wQn8w6U7z-uiOzI/edit?usp=sharing).
## Citation
To cite Orbit in publications, refer to the following whitepaper:
[Orbit: Probabilistic Forecast with Exponential Smoothing](https://arxiv.org/abs/2004.08492)
Bibtex:
```
@misc{
ng2020orbit,
title={Orbit: Probabilistic Forecast with Exponential Smoothing},
author={Edwin Ng,
Zhishi Wang,
Huigang Chen,
Steve Yang,
Slawek Smyl},
year={2020}, eprint={2004.08492}, archivePrefix={arXiv}, primaryClass={stat.CO}
}
```
## Papers
- Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip,
P., Horsfall, P., and Goodman, N. D. Pyro: Deep universal probabilistic programming. The Journal of Machine Learning
Research, 20(1):973–978, 2019.
- Hoffman, M.D. and Gelman, A. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.
J. Mach. Learn. Res., 15(1), pp.1593-1623, 2014.
- Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. Forecasting with exponential smoothing:
the state space approach. Springer Science & Business Media, 2008.
- Smyl, S. Zhang, Q. Fitting and Extending Exponential Smoothing Models with Stan.
International Symposium on Forecasting, 2015.
## Related projects
- [Pyro](https://github.com/pyro-ppl/pyro)
- [Stan](https://github.com/stan-dev/stan)
- [Rlgt](https://cran.r-project.org/web/packages/Rlgt/index.html)
- [forecast](https://github.com/robjhyndman/forecast)
- [prophet](https://facebook.github.io/prophet/)
%package help
Summary: Development documents and examples for orbit-ml
Provides: python3-orbit-ml-doc
%description help
<!--- BADGES: START --->
[][#pypi-package]
[](https://github.com/uber/orbit/actions)
[](https://orbit-ml.readthedocs.io/en/latest/?badge=latest)
[][#pypi-package]
[](https://pepy.tech/project/orbit-ml)
[][#conda-forge-feedstock]
[][#conda-forge-package]
[][#conda-forge-package]
[][#github-license]
[#github-license]: https://github.com/uber/orbit/blob/master/LICENSE
[#pypi-package]: https://pypi.org/project/orbit-ml/
[#conda-forge-package]: https://anaconda.org/conda-forge/orbit-ml
[#conda-forge-feedstock]: https://github.com/conda-forge/orbit-ml-feedstock
<!--- BADGES: END --->
# User Notice
The default page of the repo is on `dev` branch. To install the dev version, please check the section `Installing from Dev Branch`. If you are looking for a **stable version**, please refer to the `master` branch [here](https://github.com/uber/orbit/tree/master).
# Disclaimer
This project
- is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change.
- requires PyStan as a system dependency. PyStan is licensed under [GPLv3](https://www.gnu.org/licenses/gpl-3.0.html), which is a free, copyleft license for software.
# Orbit: A Python Package for Bayesian Forecasting
Orbit is a Python package for Bayesian time series forecasting and inference. It provides a
familiar and intuitive initialize-fit-predict interface for time series tasks, while utilizing probabilistic programming languages under the hood.
For details, check out our documentation and tutorials:
- HTML (stable): https://orbit-ml.readthedocs.io/en/stable/
- HTML (latest): https://orbit-ml.readthedocs.io/en/latest/
Currently, it supports concrete implementations for the following models:
- Exponential Smoothing (ETS)
- Local Global Trend (LGT)
- Damped Local Trend (DLT)
- Kernel Time-based Regression (KTR)
It also supports the following sampling/optimization methods for model estimation/inferences:
- Markov-Chain Monte Carlo (MCMC) as a full sampling method
- Maximum a Posteriori (MAP) as a point estimate method
- Variational Inference (VI) as a hybrid-sampling method on approximate
distribution
## Installation
### Installing Stable Release
Install the library either from PyPi or from the source with `pip`.
Alternatively, you can also install it from Anaconda with `conda`:
**With pip**
1. Installing from PyPI
```sh
$ pip install orbit-ml
```
2. Install from source
```sh
$ git clone https://github.com/uber/orbit.git
$ cd orbit
$ pip install -r requirements.txt
$ pip install .
```
**With conda**
The library can be installed from the conda-forge channel using conda.
```sh
$ conda install -c conda-forge orbit-ml
```
### Installing from Dev Branch
```sh
$ pip install git+https://github.com/uber/orbit.git@dev
```
## Quick Start with Damped-Local-Trend (DLT) Model
### FULL Bayesian Prediction
```python
from orbit.utils.dataset import load_iclaims
from orbit.models import DLT
from orbit.diagnostics.plot import plot_predicted_data
# log-transformed data
df = load_iclaims()
# train-test split
test_size = 52
train_df = df[:-test_size]
test_df = df[-test_size:]
dlt = DLT(
response_col='claims', date_col='week',
regressor_col=['trend.unemploy', 'trend.filling', 'trend.job'],
seasonality=52,
)
dlt.fit(df=train_df)
# outcomes data frame
predicted_df = dlt.predict(df=test_df)
plot_predicted_data(
training_actual_df=train_df, predicted_df=predicted_df,
date_col=dlt.date_col, actual_col=dlt.response_col,
test_actual_df=test_df
)
```
## Demo
Nowcasting with Regression in DLT:
[](https://colab.research.google.com/github/edwinnglabs/ts-playground/blob/master/Orbit_Tutorial.ipynb)
Backtest on M3 Data:
[](https://colab.research.google.com/github/edwinnglabs/ts-playground/blob/master/orbit_m3_backtest.ipynb)
More examples can be found under [tutorials](./docs/tutorials)
and [examples](./examples).
# Contributing
We welcome community contributors to the project. Before you start, please read our
[code of conduct](CODE_OF_CONDUCT.md) and check out
[contributing guidelines](CONTRIBUTING.md) first.
# Versioning
We document versions and changes in our [changelog](./docs/changelog.rst).
# References
## Presentations
Check out the ongoing [deck](https://docs.google.com/presentation/d/1WfTtXAW3rud4TX9HtB3NkE6buDE8tWk6BKZ2hRNXjCI/edit?usp=sharing) for scope and roadmap of the project. An older deck used in the [meet-up](https://www.meetup.com/UberEvents/events/279446143/) during July 2021 can also be found [here](https://docs.google.com/presentation/d/1R0Ol8xahIE6XlrAjAi0ewu4nRxo-wQn8w6U7z-uiOzI/edit?usp=sharing).
## Citation
To cite Orbit in publications, refer to the following whitepaper:
[Orbit: Probabilistic Forecast with Exponential Smoothing](https://arxiv.org/abs/2004.08492)
Bibtex:
```
@misc{
ng2020orbit,
title={Orbit: Probabilistic Forecast with Exponential Smoothing},
author={Edwin Ng,
Zhishi Wang,
Huigang Chen,
Steve Yang,
Slawek Smyl},
year={2020}, eprint={2004.08492}, archivePrefix={arXiv}, primaryClass={stat.CO}
}
```
## Papers
- Bingham, E., Chen, J. P., Jankowiak, M., Obermeyer, F., Pradhan, N., Karaletsos, T., Singh, R., Szerlip,
P., Horsfall, P., and Goodman, N. D. Pyro: Deep universal probabilistic programming. The Journal of Machine Learning
Research, 20(1):973–978, 2019.
- Hoffman, M.D. and Gelman, A. The No-U-Turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo.
J. Mach. Learn. Res., 15(1), pp.1593-1623, 2014.
- Hyndman, R., Koehler, A. B., Ord, J. K., and Snyder, R. D. Forecasting with exponential smoothing:
the state space approach. Springer Science & Business Media, 2008.
- Smyl, S. Zhang, Q. Fitting and Extending Exponential Smoothing Models with Stan.
International Symposium on Forecasting, 2015.
## Related projects
- [Pyro](https://github.com/pyro-ppl/pyro)
- [Stan](https://github.com/stan-dev/stan)
- [Rlgt](https://cran.r-project.org/web/packages/Rlgt/index.html)
- [forecast](https://github.com/robjhyndman/forecast)
- [prophet](https://facebook.github.io/prophet/)
%prep
%autosetup -n orbit-ml-1.1.4.2
%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-orbit-ml -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Tue May 30 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.4.2-1
- Package Spec generated
|
