automatic import of python-gluonts

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+/gluonts-0.12.6.tar.gz
diff --git a/python-gluonts.spec b/python-gluonts.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-gluonts
+Version:	0.12.6
+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/91/6a/b69d702e1cd50675974a38a0c3247774192dc7047baf729332e1482b7526/gluonts-0.12.6.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-ipykernel
+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-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
+<img class="hide-on-website" height="100px" src="https://ts.gluon.ai/dev/_static/gluonts.svg">
+
+# 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
+<img class="hide-on-website" height="100px" src="https://ts.gluon.ai/dev/_static/gluonts.svg">
+
+# 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
+<img class="hide-on-website" height="100px" src="https://ts.gluon.ai/dev/_static/gluonts.svg">
+
+# 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-0.12.6
+
+%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
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.12.6-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..ba9e86f
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+02379befd21e067323ef9dbe2eac91a4  gluonts-0.12.6.tar.gz