GluonTS: Probabilistic Time Series Models in Python
A. Alexandrov
Konstantinos Benidis
Michael Bohlke-Schneider
Valentin Flunkert
Jan Gasthaus
Tim Januschowski
Danielle C. Maddix
Syama Sundar Rangapuram
David Salinas
J. Schulz
Lorenzo Stella
Ali Caner Türkmen
Bernie Wang

Abstract
We introduce Gluon Time Series (GluonTS, available at https://gluon-ts.mxnet.io), a library for deep-learning-based time series modeling. GluonTS simplifies the development of and experimentation with time series models for common tasks such as forecasting or anomaly detection. It provides all necessary components and tools that scientists need for quickly building new models, for efficiently running and analyzing experiments and for evaluating model accuracy.
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