Metadata-Version: 2.1
Name: gluonts
Version: 0.14.2
Summary: Probabilistic time series modeling in Python.
Home-page: https://github.com/awslabs/gluonts/
Author: Amazon
Author-email: gluon-ts-dev@amazon.com
Maintainer-email: gluon-ts-dev@amazon.com
License: Apache License 2.0
Project-URL: Documentation, https://ts.gluon.ai/stable/
Project-URL: Source Code, https://github.com/awslabs/gluonts/
Description: <img class="hide-on-website" height="100px" src="https://ts.gluon.ai/dev/_static/gluonts.svg">
        
        # GluonTS - Probabilistic Time Series Modeling in Python
        
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        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
        # install with support for torch models
        pip install "gluonts[torch]"
        
        # install with support for mxnet models
        pip install "gluonts[mxnet]"
        ```
        
        See the [documentation](https://ts.gluon.ai/stable/getting_started/install.html)
        for more info on how GluonTS can be installed.
        
        ## Simple Example
        
        To illustrate how to use GluonTS, we train a DeepAR-model and make predictions
        using the airpassengers dataset. The dataset consists of a single time
        series of monthly passenger numbers between 1949 and 1960. We train the model
        on the first nine years and make predictions for the remaining three years.
        
        ```py
        import pandas as pd
        import matplotlib.pyplot as plt
        
        from gluonts.dataset.pandas import PandasDataset
        from gluonts.dataset.split import split
        from gluonts.torch import DeepAREstimator
        
        # 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")
        
        # Split the data for training and testing
        training_data, test_gen = split(dataset, offset=-36)
        test_data = test_gen.generate_instances(prediction_length=12, windows=3)
        
        # Train the model and make predictions
        model = DeepAREstimator(
            prediction_length=12, freq="M", trainer_kwargs={"max_epochs": 5}
        ).train(training_data)
        
        forecasts = list(model.predict(test_data.input))
        
        # Plot predictions
        plt.plot(df["1954":], color="black")
        for forecast in forecasts:
          forecast.plot()
        plt.legend(["True values"], loc="upper left", fontsize="xx-large")
        plt.show()
        ```
        
        ![[train-test]](https://ts.gluon.ai/static/README/forecasts.png)
        
        Note, the forecasts are displayed in terms of a probability distribution and
        the shaded areas represent the 50% and 90% prediction intervals.
        
        
        ## 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/)
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: arrow
Provides-Extra: dev
Provides-Extra: docs
Provides-Extra: mxnet
Provides-Extra: R
Provides-Extra: Prophet
Provides-Extra: pro
Provides-Extra: shell
Provides-Extra: torch
