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https://github.com/awslabs/gluon-ts

Probabilistic time series modeling in Python
https://github.com/awslabs/gluon-ts

artificial-intelligence aws data-science deep-learning forecasting machine-learning mxnet neural-networks pytorch sagemaker time-series time-series-forecasting time-series-prediction timeseries torch

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Probabilistic time series modeling in Python

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README 

        



# GluonTS - Probabilistic Time Series Modeling in Python



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[![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/)

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**📢 BREAKING NEWS**: We released **Chronos**, a suite of pretrained models for zero-shot time series forecasting. Chronos can generate accurate probabilistic predictions for new time series not seen during training. Check it out [here](https://github.com/amazon-science/chronos-forecasting)!



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/)