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GluonTS: Probabilistic Time Series Models in Python
lib:e4d8a8eac9285c65 (v1.0.0)
Authors: Alexander Alexandrov,Konstantinos Benidis,Michael Bohlke-Schneider,Valentin Flunkert,Jan Gasthaus,Tim Januschowski,Danielle C. Maddix,Syama Rangapuram,David Salinas,Jasper Schulz,Lorenzo Stella,Ali Caner Türkmen,Yuyang Wang
ArXiv: 1906.05264
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/1906.05264v2
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.
ArXiv: 1906.05264
Document: PDF DOI
Artifact development version: GitHub
Abstract URL: https://arxiv.org/abs/1906.05264v2
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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