GluonTS: Probabilistic Time Series Models in Python
This library addresses the need for efficient time series modeling tools for researchers and practitioners, but it is incremental as it builds on existing deep learning methods without introducing new paradigms.
The authors introduced GluonTS, a Python library for deep-learning-based time series modeling, which simplifies development and experimentation for tasks like forecasting or anomaly detection by providing necessary components and tools for building models and evaluating accuracy.
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.