%global _empty_manifest_terminate_build 0 Name: python-gluonts Version: 0.12.7 Release: 1 Summary: Probabilistic time series modeling in Python. License: Apache License 2.0 URL: https://github.com/awslabs/gluonts/ Source0: https://mirrors.nju.edu.cn/pypi/web/packages/7d/d7/32ce8ef7371399230c301e1d2d9bbffb5b962516afc9f4ce5439a25843fc/gluonts-0.12.7.tar.gz BuildArch: noarch Requires: python3-numpy Requires: python3-pandas Requires: python3-pydantic Requires: python3-tqdm Requires: python3-toolz Requires: python3-typing-extensions Requires: python3-prophet Requires: python3-rpy2 Requires: python3-pyarrow Requires: python3-pyarrow Requires: python3-ipython Requires: python3-ipykernel Requires: python3-nbconvert Requires: python3-nbsphinx Requires: python3-notedown Requires: python3-pytest-runner Requires: python3-recommonmark Requires: python3-sphinx Requires: python3-docutils Requires: python3-optuna Requires: python3-furo Requires: python3-m2r2 Requires: python3-myst-parser Requires: python3-click Requires: python3-orjson Requires: python3-black Requires: python3-holidays Requires: python3-matplotlib Requires: python3-pandas Requires: python3-flaky Requires: python3-pytest-cov Requires: python3-pytest-timeout Requires: python3-pytest-xdist Requires: python3-pytest Requires: python3-ujson Requires: python3-requests Requires: python3-flask Requires: python3-gunicorn Requires: python3-sagemaker Requires: python3-s3fs Requires: python3-fsspec Requires: python3-pyarrow Requires: python3-pyarrow Requires: python3-s3fs Requires: python3-ipython Requires: python3-ipykernel Requires: python3-nbconvert Requires: python3-nbsphinx Requires: python3-notedown Requires: python3-pytest-runner Requires: python3-recommonmark Requires: python3-sphinx Requires: python3-docutils Requires: python3-optuna Requires: python3-furo Requires: python3-m2r2 Requires: python3-myst-parser Requires: python3-click Requires: python3-orjson Requires: python3-black Requires: python3-holidays Requires: python3-matplotlib Requires: python3-numpy Requires: python3-mxnet Requires: python3-orjson Requires: python3-pyarrow Requires: python3-pyarrow Requires: python3-flask Requires: python3-gunicorn Requires: python3-torch Requires: python3-pytorch-lightning Requires: python3-protobuf Requires: python3-scipy Requires: python3-scipy %description # GluonTS - Probabilistic Time Series Modeling in Python [![PyPI](https://img.shields.io/pypi/v/gluonts.svg?style=flat-square&color=b75347)](https://pypi.org/project/gluonts/) [![GitHub](https://img.shields.io/github/license/awslabs/gluonts.svg?style=flat-square&color=df7e66)](./LICENSE) [![Static](https://img.shields.io/static/v1?label=docs&message=stable&color=edc775&style=flat-square)](https://ts.gluon.ai/) [![Static](https://img.shields.io/static/v1?label=docs&message=dev&color=edc775&style=flat-square)](https://ts.gluon.ai/dev/) [![PyPI Downloads](https://img.shields.io/pypi/dm/gluonts?style=flat-square&color=94b594)](https://pepy.tech/project/gluonts) GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models, based on [PyTorch](https://pytorch.org) and [MXNet](https://mxnet.apache.org). ## Installation GluonTS requires Python 3.7 or newer, and the easiest way to install it is via `pip`: ```bash # support for mxnet models, faster datasets pip install "gluonts[mxnet,pro]" # support for torch models, faster datasets pip install "gluonts[torch,pro]" ``` ## Simple Example To illustrate how to use GluonTS, we train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts, by removing the last three years (36 month) from the train data. Thus, we will train a model on just the first nine years of data. ```py import pandas as pd import matplotlib.pyplot as plt from gluonts.dataset.pandas import PandasDataset from gluonts.dataset.split import split from gluonts.mx import DeepAREstimator, Trainer # Load data from a CSV file into a PandasDataset df = pd.read_csv( "https://raw.githubusercontent.com/AileenNielsen/" "TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv", index_col=0, parse_dates=True, ) dataset = PandasDataset(df, target="#Passengers") # Train a DeepAR model on all data but the last 36 months training_data, test_gen = split(dataset, offset=-36) model = DeepAREstimator( prediction_length=12, freq="M", trainer=Trainer(epochs=5) ).train(training_data) # Generate test instances and predictions for them test_data = test_gen.generate_instances(prediction_length=12, windows=3) forecasts = list(model.predict(test_data.input)) # Plot predictions df["#Passengers"].plot(color="black") for forecast, color in zip(forecasts, ["green", "blue", "purple"]): forecast.plot(color=f"tab:{color}") plt.legend(["True values"], loc="upper left", fontsize="xx-large") ``` ![[train-test]](https://d2kv9n23y3w0pn.cloudfront.net/static/README/forecasts.png) Note that the forecasts are displayed in terms of a probability distribution: The shaded areas represent the 50% and 90% prediction intervals, respectively, centered around the median. ## Contributing If you wish to contribute to the project, please refer to our [contribution guidelines](https://github.com/awslabs/gluonts/tree/dev/CONTRIBUTING.md). ## Citing If you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers, in addition to any model-specific references that are relevant for your work: ```bibtex @article{gluonts_jmlr, author = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang}, title = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {116}, pages = {1-6}, url = {http://jmlr.org/papers/v21/19-820.html} } ``` ```bibtex @article{gluonts_arxiv, author = {Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. and Flunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C. and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. and Türkmen, A. C. and Wang, Y.}, title = {{GluonTS: Probabilistic Time Series Modeling in Python}}, journal = {arXiv preprint arXiv:1906.05264}, year = {2019} } ``` ## Links ### Documentation * [Documentation (stable)](https://ts.gluon.ai/stable/) * [Documentation (development)](https://ts.gluon.ai/dev/) ### References * [JMLR MLOSS Paper](http://www.jmlr.org/papers/v21/19-820.html) * [ArXiv Paper](https://arxiv.org/abs/1906.05264) * [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluonts/tree/dev/REFERENCES.md): a bibliography. ### Tutorials and Workshops * [Tutorial at IJCAI 2021 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-IJCAI-2021/) with [YouTube link](https://youtu.be/AB3I9pdT46c). * [Tutorial at WWW 2020 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-WWW-2020/) * [Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) * [Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) * [Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) * [Neural Time Series with GluonTS](https://youtu.be/beEJMIt9xJ8) * [International Symposium of Forecasting: Deep Learning for Forecasting workshop](https://lostella.github.io/ISF-2020-Deep-Learning-Workshop/) %package -n python3-gluonts Summary: Probabilistic time series modeling in Python. Provides: python-gluonts BuildRequires: python3-devel BuildRequires: python3-setuptools BuildRequires: python3-pip %description -n python3-gluonts # GluonTS - Probabilistic Time Series Modeling in Python [![PyPI](https://img.shields.io/pypi/v/gluonts.svg?style=flat-square&color=b75347)](https://pypi.org/project/gluonts/) [![GitHub](https://img.shields.io/github/license/awslabs/gluonts.svg?style=flat-square&color=df7e66)](./LICENSE) [![Static](https://img.shields.io/static/v1?label=docs&message=stable&color=edc775&style=flat-square)](https://ts.gluon.ai/) [![Static](https://img.shields.io/static/v1?label=docs&message=dev&color=edc775&style=flat-square)](https://ts.gluon.ai/dev/) [![PyPI Downloads](https://img.shields.io/pypi/dm/gluonts?style=flat-square&color=94b594)](https://pepy.tech/project/gluonts) GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models, based on [PyTorch](https://pytorch.org) and [MXNet](https://mxnet.apache.org). ## Installation GluonTS requires Python 3.7 or newer, and the easiest way to install it is via `pip`: ```bash # support for mxnet models, faster datasets pip install "gluonts[mxnet,pro]" # support for torch models, faster datasets pip install "gluonts[torch,pro]" ``` ## Simple Example To illustrate how to use GluonTS, we train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts, by removing the last three years (36 month) from the train data. Thus, we will train a model on just the first nine years of data. ```py import pandas as pd import matplotlib.pyplot as plt from gluonts.dataset.pandas import PandasDataset from gluonts.dataset.split import split from gluonts.mx import DeepAREstimator, Trainer # Load data from a CSV file into a PandasDataset df = pd.read_csv( "https://raw.githubusercontent.com/AileenNielsen/" "TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv", index_col=0, parse_dates=True, ) dataset = PandasDataset(df, target="#Passengers") # Train a DeepAR model on all data but the last 36 months training_data, test_gen = split(dataset, offset=-36) model = DeepAREstimator( prediction_length=12, freq="M", trainer=Trainer(epochs=5) ).train(training_data) # Generate test instances and predictions for them test_data = test_gen.generate_instances(prediction_length=12, windows=3) forecasts = list(model.predict(test_data.input)) # Plot predictions df["#Passengers"].plot(color="black") for forecast, color in zip(forecasts, ["green", "blue", "purple"]): forecast.plot(color=f"tab:{color}") plt.legend(["True values"], loc="upper left", fontsize="xx-large") ``` ![[train-test]](https://d2kv9n23y3w0pn.cloudfront.net/static/README/forecasts.png) Note that the forecasts are displayed in terms of a probability distribution: The shaded areas represent the 50% and 90% prediction intervals, respectively, centered around the median. ## Contributing If you wish to contribute to the project, please refer to our [contribution guidelines](https://github.com/awslabs/gluonts/tree/dev/CONTRIBUTING.md). ## Citing If you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers, in addition to any model-specific references that are relevant for your work: ```bibtex @article{gluonts_jmlr, author = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang}, title = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {116}, pages = {1-6}, url = {http://jmlr.org/papers/v21/19-820.html} } ``` ```bibtex @article{gluonts_arxiv, author = {Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. and Flunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C. and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. and Türkmen, A. C. and Wang, Y.}, title = {{GluonTS: Probabilistic Time Series Modeling in Python}}, journal = {arXiv preprint arXiv:1906.05264}, year = {2019} } ``` ## Links ### Documentation * [Documentation (stable)](https://ts.gluon.ai/stable/) * [Documentation (development)](https://ts.gluon.ai/dev/) ### References * [JMLR MLOSS Paper](http://www.jmlr.org/papers/v21/19-820.html) * [ArXiv Paper](https://arxiv.org/abs/1906.05264) * [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluonts/tree/dev/REFERENCES.md): a bibliography. ### Tutorials and Workshops * [Tutorial at IJCAI 2021 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-IJCAI-2021/) with [YouTube link](https://youtu.be/AB3I9pdT46c). * [Tutorial at WWW 2020 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-WWW-2020/) * [Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) * [Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) * [Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) * [Neural Time Series with GluonTS](https://youtu.be/beEJMIt9xJ8) * [International Symposium of Forecasting: Deep Learning for Forecasting workshop](https://lostella.github.io/ISF-2020-Deep-Learning-Workshop/) %package help Summary: Development documents and examples for gluonts Provides: python3-gluonts-doc %description help # GluonTS - Probabilistic Time Series Modeling in Python [![PyPI](https://img.shields.io/pypi/v/gluonts.svg?style=flat-square&color=b75347)](https://pypi.org/project/gluonts/) [![GitHub](https://img.shields.io/github/license/awslabs/gluonts.svg?style=flat-square&color=df7e66)](./LICENSE) [![Static](https://img.shields.io/static/v1?label=docs&message=stable&color=edc775&style=flat-square)](https://ts.gluon.ai/) [![Static](https://img.shields.io/static/v1?label=docs&message=dev&color=edc775&style=flat-square)](https://ts.gluon.ai/dev/) [![PyPI Downloads](https://img.shields.io/pypi/dm/gluonts?style=flat-square&color=94b594)](https://pepy.tech/project/gluonts) GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models, based on [PyTorch](https://pytorch.org) and [MXNet](https://mxnet.apache.org). ## Installation GluonTS requires Python 3.7 or newer, and the easiest way to install it is via `pip`: ```bash # support for mxnet models, faster datasets pip install "gluonts[mxnet,pro]" # support for torch models, faster datasets pip install "gluonts[torch,pro]" ``` ## Simple Example To illustrate how to use GluonTS, we train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months). We split the dataset into train and test parts, by removing the last three years (36 month) from the train data. Thus, we will train a model on just the first nine years of data. ```py import pandas as pd import matplotlib.pyplot as plt from gluonts.dataset.pandas import PandasDataset from gluonts.dataset.split import split from gluonts.mx import DeepAREstimator, Trainer # Load data from a CSV file into a PandasDataset df = pd.read_csv( "https://raw.githubusercontent.com/AileenNielsen/" "TimeSeriesAnalysisWithPython/master/data/AirPassengers.csv", index_col=0, parse_dates=True, ) dataset = PandasDataset(df, target="#Passengers") # Train a DeepAR model on all data but the last 36 months training_data, test_gen = split(dataset, offset=-36) model = DeepAREstimator( prediction_length=12, freq="M", trainer=Trainer(epochs=5) ).train(training_data) # Generate test instances and predictions for them test_data = test_gen.generate_instances(prediction_length=12, windows=3) forecasts = list(model.predict(test_data.input)) # Plot predictions df["#Passengers"].plot(color="black") for forecast, color in zip(forecasts, ["green", "blue", "purple"]): forecast.plot(color=f"tab:{color}") plt.legend(["True values"], loc="upper left", fontsize="xx-large") ``` ![[train-test]](https://d2kv9n23y3w0pn.cloudfront.net/static/README/forecasts.png) Note that the forecasts are displayed in terms of a probability distribution: The shaded areas represent the 50% and 90% prediction intervals, respectively, centered around the median. ## Contributing If you wish to contribute to the project, please refer to our [contribution guidelines](https://github.com/awslabs/gluonts/tree/dev/CONTRIBUTING.md). ## Citing If you use GluonTS in a scientific publication, we encourage you to add the following references to the related papers, in addition to any model-specific references that are relevant for your work: ```bibtex @article{gluonts_jmlr, author = {Alexander Alexandrov and Konstantinos Benidis and Michael Bohlke-Schneider and Valentin Flunkert and Jan Gasthaus and Tim Januschowski and Danielle C. Maddix and Syama Rangapuram and David Salinas and Jasper Schulz and Lorenzo Stella and Ali Caner Türkmen and Yuyang Wang}, title = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}}, journal = {Journal of Machine Learning Research}, year = {2020}, volume = {21}, number = {116}, pages = {1-6}, url = {http://jmlr.org/papers/v21/19-820.html} } ``` ```bibtex @article{gluonts_arxiv, author = {Alexandrov, A. and Benidis, K. and Bohlke-Schneider, M. and Flunkert, V. and Gasthaus, J. and Januschowski, T. and Maddix, D. C. and Rangapuram, S. and Salinas, D. and Schulz, J. and Stella, L. and Türkmen, A. C. and Wang, Y.}, title = {{GluonTS: Probabilistic Time Series Modeling in Python}}, journal = {arXiv preprint arXiv:1906.05264}, year = {2019} } ``` ## Links ### Documentation * [Documentation (stable)](https://ts.gluon.ai/stable/) * [Documentation (development)](https://ts.gluon.ai/dev/) ### References * [JMLR MLOSS Paper](http://www.jmlr.org/papers/v21/19-820.html) * [ArXiv Paper](https://arxiv.org/abs/1906.05264) * [Collected Papers from the group behind GluonTS](https://github.com/awslabs/gluonts/tree/dev/REFERENCES.md): a bibliography. ### Tutorials and Workshops * [Tutorial at IJCAI 2021 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-IJCAI-2021/) with [YouTube link](https://youtu.be/AB3I9pdT46c). * [Tutorial at WWW 2020 (with videos)](https://lovvge.github.io/Forecasting-Tutorial-WWW-2020/) * [Tutorial at SIGMOD 2019](https://lovvge.github.io/Forecasting-Tutorials/SIGMOD-2019/) * [Tutorial at KDD 2019](https://lovvge.github.io/Forecasting-Tutorial-KDD-2019/) * [Tutorial at VLDB 2018](https://lovvge.github.io/Forecasting-Tutorial-VLDB-2018/) * [Neural Time Series with GluonTS](https://youtu.be/beEJMIt9xJ8) * [International Symposium of Forecasting: Deep Learning for Forecasting workshop](https://lostella.github.io/ISF-2020-Deep-Learning-Workshop/) %prep %autosetup -n gluonts-0.12.7 %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-gluonts -f filelist.lst %dir %{python3_sitelib}/* %files help -f doclist.lst %{_docdir}/* %changelog * Sun Apr 23 2023 Python_Bot - 0.12.7-1 - Package Spec generated