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2020 JMLR JMLR 2020

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2020. (edit, beta)

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🐣 Hot Topic Early Bird — time series forecasting
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

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 Turkmen , Yuyang Wang

Topics

Machine Learning > Learning Types > Self-Supervised Learning Data Science & Analytics > Methods > Time Series

Keywords

probabilistic modeling anomaly detection deep learning time series forecasting neural network
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