%global _empty_manifest_terminate_build 0 Name: python-etna-ts Version: 1.3.1 Release: 1 Summary: ETNA is the first python open source framework of Tinkoff.ru AI Center. It is designed to make working with time series simple, productive, and fun. License: Apache 2.0 URL: https://github.com/tinkoff-ai/etna-ts Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a7/be/b7e2e3cbc2e7dd0603c42aa4a1be3e4852313eef86abf2c1c4eb3d71c408/etna-ts-1.3.1.tar.gz BuildArch: noarch Requires: python3-scikit-learn Requires: python3-pandas Requires: python3-catboost Requires: python3-ruptures Requires: python3-numba Requires: python3-seaborn Requires: python3-statsmodels Requires: python3-dill Requires: python3-toml Requires: python3-loguru Requires: python3-saxpy Requires: python3-hydra-slayer Requires: python3-typer Requires: python3-prophet Requires: python3-torch Requires: python3-pytorch-forecasting Requires: python3-wandb Requires: python3-sphinx-mathjax-offline Requires: python3-nbsphinx Requires: python3-Sphinx Requires: python3-numpydoc Requires: python3-sphinx-rtd-theme Requires: python3-myst-parser Requires: python3-GitPython Requires: python3-pytest Requires: python3-coverage Requires: python3-pytest-cov Requires: python3-black Requires: python3-isort Requires: python3-flake8 Requires: python3-pep8-naming Requires: python3-flake8-docstrings Requires: python3-mypy Requires: python3-types-PyYAML Requires: python3-click Requires: python3-semver Requires: python3-ipywidgets Requires: python3-jupyter Requires: python3-nbconvert Requires: python3-omegaconf %description # ETNA Time Series Library [![Pipi version](https://img.shields.io/pypi/v/etna-ts.svg)](https://pypi.org/project/etna-ts/) [![PyPI Status](https://static.pepy.tech/personalized-badge/etna-ts?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/etna-ts) [![Coverage](https://img.shields.io/codecov/c/github/tinkoff-ai/etna-ts)](https://codecov.io/gh/tinkoff-ai/etna-ts) [![Telegram](https://img.shields.io/badge/channel-telegram-blue)](https://t.me/etna_support) [Homepage](https://etna.tinkoff.ru) | [Documentation](https://etna-docs.netlify.app/) | [Tutorials](https://github.com/tinkoff-ai/etna-ts/tree/master/examples) | [Contribution Guide](https://github.com/tinkoff-ai/etna-ts/blob/master/CONTRIBUTING.md) | [Release Notes](https://github.com/tinkoff-ai/etna-ts/releases) ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from classic machine learning to SOTA neural networks, models combination methods and smart backtesting. ETNA is designed to make working with time series simple, productive, and fun. ETNA is the first python open source framework of [Tinkoff.ru](https://www.tinkoff.ru/eng/) Artificial Intelligence Center. The library started as an internal product in our company - we use it in over 10+ projects now, so we often release updates. Contributions are welcome - check our [Contribution Guide](https://github.com/tinkoff-ai/etna-ts/blob/master/CONTRIBUTING.md). ## Installation ETNA is on [PyPI](https://pypi.org/project/etna-ts), so you can use `pip` to install it. ```bash pip install --upgrade pip pip install etna-ts ``` ## Get started Here's some example code for a quick start. ```python import pandas as pd from etna.datasets.tsdataset import TSDataset from etna.models import ProphetModel # Read the data df = pd.read_csv("examples/data/example_dataset.csv") # Create a TSDataset df = TSDataset.to_dataset(df) ts = TSDataset(df, freq="D") # Choose a horizon HORIZON = 8 # Fit the model model = ProphetModel() model.fit(ts) # Make the forecast future_ts = ts.make_future(HORIZON) forecast_ts = model.forecast(future_ts) ``` ## Tutorials We have also prepared a set of tutorials for an easy introduction: | Notebook | Interactive launch | |:----------|------:| | [Get started](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/get_started.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/get_started.ipynb) | | [Backtest](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/backtest.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/backtest.ipynb) | | [EDA](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/EDA.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/EDA.ipynb) | | [Outliers](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/outliers.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/outliers.ipynb) | | [Clustering](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/clustering.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/clustering.ipynb) | | [Deep learning models](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/NN_examples.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/NN_examples.ipynb) | | [Ensembles](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/ensembles.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/ensembles.ipynb) | ## Documentation ETNA documentation is available [here](https://etna-docs.netlify.app/). ## Acknowledgments ### ETNA.Team [Alekseev Andrey](https://github.com/iKintosh), [Shenshina Julia](https://github.com/julia-shenshina), [Gabdushev Martin](https://github.com/martins0n), [Kolesnikov Sergey](https://github.com/Scitator), [Bunin Dmitriy](https://github.com/Mr-Geekman), [Chikov Aleksandr](https://github.com/alex-hse-repository), [Barinov Nikita](https://github.com/diadorer), [Romantsov Nikolay](https://github.com/WinstonDovlatov), [Makhin Artem](https://github.com/Ama16), [Denisov Vladislav](https://github.com/v-v-denisov), [Mitskovets Ivan](https://github.com/imitskovets), [Munirova Albina](https://github.com/albinamunirova) ### ETNA.Contributors [Levashov Artem](https://github.com/soft1q), [Podkidyshev Aleksey](https://github.com/alekseyen) ## License Feel free to use our library in your commercial and private applications. ETNA is covered by [Apache 2.0](/LICENSE). Read more about this license [here](https://choosealicense.com/licenses/apache-2.0/) %package -n python3-etna-ts Summary: ETNA is the first python open source framework of Tinkoff.ru AI Center. It is designed to make working with time series simple, productive, and fun. Provides: python-etna-ts BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-etna-ts # ETNA Time Series Library [![Pipi version](https://img.shields.io/pypi/v/etna-ts.svg)](https://pypi.org/project/etna-ts/) [![PyPI Status](https://static.pepy.tech/personalized-badge/etna-ts?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/etna-ts) [![Coverage](https://img.shields.io/codecov/c/github/tinkoff-ai/etna-ts)](https://codecov.io/gh/tinkoff-ai/etna-ts) [![Telegram](https://img.shields.io/badge/channel-telegram-blue)](https://t.me/etna_support) [Homepage](https://etna.tinkoff.ru) | [Documentation](https://etna-docs.netlify.app/) | [Tutorials](https://github.com/tinkoff-ai/etna-ts/tree/master/examples) | [Contribution Guide](https://github.com/tinkoff-ai/etna-ts/blob/master/CONTRIBUTING.md) | [Release Notes](https://github.com/tinkoff-ai/etna-ts/releases) ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from classic machine learning to SOTA neural networks, models combination methods and smart backtesting. ETNA is designed to make working with time series simple, productive, and fun. ETNA is the first python open source framework of [Tinkoff.ru](https://www.tinkoff.ru/eng/) Artificial Intelligence Center. The library started as an internal product in our company - we use it in over 10+ projects now, so we often release updates. Contributions are welcome - check our [Contribution Guide](https://github.com/tinkoff-ai/etna-ts/blob/master/CONTRIBUTING.md). ## Installation ETNA is on [PyPI](https://pypi.org/project/etna-ts), so you can use `pip` to install it. ```bash pip install --upgrade pip pip install etna-ts ``` ## Get started Here's some example code for a quick start. ```python import pandas as pd from etna.datasets.tsdataset import TSDataset from etna.models import ProphetModel # Read the data df = pd.read_csv("examples/data/example_dataset.csv") # Create a TSDataset df = TSDataset.to_dataset(df) ts = TSDataset(df, freq="D") # Choose a horizon HORIZON = 8 # Fit the model model = ProphetModel() model.fit(ts) # Make the forecast future_ts = ts.make_future(HORIZON) forecast_ts = model.forecast(future_ts) ``` ## Tutorials We have also prepared a set of tutorials for an easy introduction: | Notebook | Interactive launch | |:----------|------:| | [Get started](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/get_started.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/get_started.ipynb) | | [Backtest](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/backtest.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/backtest.ipynb) | | [EDA](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/EDA.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/EDA.ipynb) | | [Outliers](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/outliers.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/outliers.ipynb) | | [Clustering](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/clustering.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/clustering.ipynb) | | [Deep learning models](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/NN_examples.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/NN_examples.ipynb) | | [Ensembles](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/ensembles.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/ensembles.ipynb) | ## Documentation ETNA documentation is available [here](https://etna-docs.netlify.app/). ## Acknowledgments ### ETNA.Team [Alekseev Andrey](https://github.com/iKintosh), [Shenshina Julia](https://github.com/julia-shenshina), [Gabdushev Martin](https://github.com/martins0n), [Kolesnikov Sergey](https://github.com/Scitator), [Bunin Dmitriy](https://github.com/Mr-Geekman), [Chikov Aleksandr](https://github.com/alex-hse-repository), [Barinov Nikita](https://github.com/diadorer), [Romantsov Nikolay](https://github.com/WinstonDovlatov), [Makhin Artem](https://github.com/Ama16), [Denisov Vladislav](https://github.com/v-v-denisov), [Mitskovets Ivan](https://github.com/imitskovets), [Munirova Albina](https://github.com/albinamunirova) ### ETNA.Contributors [Levashov Artem](https://github.com/soft1q), [Podkidyshev Aleksey](https://github.com/alekseyen) ## License Feel free to use our library in your commercial and private applications. ETNA is covered by [Apache 2.0](/LICENSE). Read more about this license [here](https://choosealicense.com/licenses/apache-2.0/) %package help Summary: Development documents and examples for etna-ts Provides: python3-etna-ts-doc %description help # ETNA Time Series Library [![Pipi version](https://img.shields.io/pypi/v/etna-ts.svg)](https://pypi.org/project/etna-ts/) [![PyPI Status](https://static.pepy.tech/personalized-badge/etna-ts?period=total&units=international_system&left_color=grey&right_color=green&left_text=Downloads)](https://pepy.tech/project/etna-ts) [![Coverage](https://img.shields.io/codecov/c/github/tinkoff-ai/etna-ts)](https://codecov.io/gh/tinkoff-ai/etna-ts) [![Telegram](https://img.shields.io/badge/channel-telegram-blue)](https://t.me/etna_support) [Homepage](https://etna.tinkoff.ru) | [Documentation](https://etna-docs.netlify.app/) | [Tutorials](https://github.com/tinkoff-ai/etna-ts/tree/master/examples) | [Contribution Guide](https://github.com/tinkoff-ai/etna-ts/blob/master/CONTRIBUTING.md) | [Release Notes](https://github.com/tinkoff-ai/etna-ts/releases) ETNA is an easy-to-use time series forecasting framework. It includes built in toolkits for time series preprocessing, feature generation, a variety of predictive models with unified interface - from classic machine learning to SOTA neural networks, models combination methods and smart backtesting. ETNA is designed to make working with time series simple, productive, and fun. ETNA is the first python open source framework of [Tinkoff.ru](https://www.tinkoff.ru/eng/) Artificial Intelligence Center. The library started as an internal product in our company - we use it in over 10+ projects now, so we often release updates. Contributions are welcome - check our [Contribution Guide](https://github.com/tinkoff-ai/etna-ts/blob/master/CONTRIBUTING.md). ## Installation ETNA is on [PyPI](https://pypi.org/project/etna-ts), so you can use `pip` to install it. ```bash pip install --upgrade pip pip install etna-ts ``` ## Get started Here's some example code for a quick start. ```python import pandas as pd from etna.datasets.tsdataset import TSDataset from etna.models import ProphetModel # Read the data df = pd.read_csv("examples/data/example_dataset.csv") # Create a TSDataset df = TSDataset.to_dataset(df) ts = TSDataset(df, freq="D") # Choose a horizon HORIZON = 8 # Fit the model model = ProphetModel() model.fit(ts) # Make the forecast future_ts = ts.make_future(HORIZON) forecast_ts = model.forecast(future_ts) ``` ## Tutorials We have also prepared a set of tutorials for an easy introduction: | Notebook | Interactive launch | |:----------|------:| | [Get started](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/get_started.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/get_started.ipynb) | | [Backtest](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/backtest.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/backtest.ipynb) | | [EDA](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/EDA.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/EDA.ipynb) | | [Outliers](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/outliers.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/outliers.ipynb) | | [Clustering](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/clustering.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/clustering.ipynb) | | [Deep learning models](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/NN_examples.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/NN_examples.ipynb) | | [Ensembles](https://github.com/tinkoff-ai/etna-ts/tree/master/examples/ensembles.ipynb) | [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/tinkoff-ai/etna-ts/master?filepath=examples/ensembles.ipynb) | ## Documentation ETNA documentation is available [here](https://etna-docs.netlify.app/). ## Acknowledgments ### ETNA.Team [Alekseev Andrey](https://github.com/iKintosh), [Shenshina Julia](https://github.com/julia-shenshina), [Gabdushev Martin](https://github.com/martins0n), [Kolesnikov Sergey](https://github.com/Scitator), [Bunin Dmitriy](https://github.com/Mr-Geekman), [Chikov Aleksandr](https://github.com/alex-hse-repository), [Barinov Nikita](https://github.com/diadorer), [Romantsov Nikolay](https://github.com/WinstonDovlatov), [Makhin Artem](https://github.com/Ama16), [Denisov Vladislav](https://github.com/v-v-denisov), [Mitskovets Ivan](https://github.com/imitskovets), [Munirova Albina](https://github.com/albinamunirova) ### ETNA.Contributors [Levashov Artem](https://github.com/soft1q), [Podkidyshev Aleksey](https://github.com/alekseyen) ## License Feel free to use our library in your commercial and private applications. ETNA is covered by [Apache 2.0](/LICENSE). Read more about this license [here](https://choosealicense.com/licenses/apache-2.0/) %prep %autosetup -n etna-ts-1.3.1 %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-etna-ts -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Wed May 31 2023 Python_Bot - 1.3.1-1 - Package Spec generated