GluonTS: Probabilistic and Neural Time Series Modeling in Python
Abstract
We introduce the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection. GluonTS simplifies the time series modeling pipeline by providing the necessary components and tools for quick model development, efficient experimentation and evaluation. In addition, it contains reference implementations of state-of-the-art time series models that enable simple benchmarking of new algorithms.
Cite
Text
Alexandrov et al. "GluonTS: Probabilistic and Neural Time Series Modeling in Python." Machine Learning Open Source Software, 2020.Markdown
[Alexandrov et al. "GluonTS: Probabilistic and Neural Time Series Modeling in Python." Machine Learning Open Source Software, 2020.](https://mlanthology.org/mloss/2020/alexandrov2020jmlr-gluonts/)BibTeX
@article{alexandrov2020jmlr-gluonts,
title = {{GluonTS: Probabilistic and Neural Time Series Modeling in Python}},
author = {Alexandrov, Alexander and Benidis, Konstantinos and Bohlke-Schneider, Michael and Flunkert, Valentin and Gasthaus, Jan and Januschowski, Tim and Maddix, Danielle C. and Rangapuram, Syama and Salinas, David and Schulz, Jasper and Stella, Lorenzo and Türkmen, Ali Caner and Wang, Yuyang},
journal = {Machine Learning Open Source Software},
year = {2020},
pages = {1-6},
volume = {21},
url = {https://mlanthology.org/mloss/2020/alexandrov2020jmlr-gluonts/}
}