Metadata-Version: 2.1
Name: gluonts
Version: 0.12.0rc1
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
        
        [![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/)
        
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
