GluonTS: Probabilistic Time Series Models in Python
By A. Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Sundar Rangapuram, David Salinas, J. Schulz and 3 more. 2019
131 citations recorded by the source corpus.
We introduce Gluon Time Series (GluonTS, available at this https URL), 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.
Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Fotios Petropoulos
- Christoph Bergmeir
- Spyros Makridakis
- Tim Januschowski
- Jan Gasthaus
- Nikolaos Kourentzes
- Hsiang-Fu Yu
- Bernie Wang
In Management Science and Operations Research
- Fotios Petropoulos
- Rob J Hyndman
- Konstantinos Nikolopoulos
- Nikolaos Kourentzes
- Vassilios Assimakopoulos
- Spyros Makridakis
- Christoph Bergmeir
- Evangelos Spiliotis
In Signal Processing
- Bernie Wang
- Vassilios Assimakopoulos
- Konstantinos Nikolopoulos
- Christoph Bergmeir
- Tim Januschowski
- Evangelos Spiliotis
- Hansika Hewamalage
- Spyros Makridakis
Citations in the corpus
- High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula Processes (2019, methodology): “We re-implemented the model described in this paper in GluonTS [1], an open-source time series toolkit.”
- Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting (2019, methodology): “DeepAR: In all datasets, we use the default parameters in the DeepAREstimator in the GluonTS implementation.”
- Deep Learning for Forecasting (2020, methodology): “Finally, we will introduce GluonTS [1], a time series modelling toolkit primarily aimed at forecasting which is available in open-source.”
- Forecasting: theory and practice (2020, methodology): “…complex real-world forecasting situations such as data sparsity and cold-starts (Chen et al., 2020), include exogenous covariates such as promotional information and keyword search indices (Law et al., 2019), and allow for different choices of distributional assumptions (Salinas et al., 2019a).”
- Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019, background): “GluonTS, a Python based open-source forecasting library recently introduced by Alexandrov et al. (2019) is specifically designed for the purpose of supporting forecasting researchers with easy experimentation using deep neural networks.”