mlsanityData to 7 Oct 2026

GluonTS: Probabilistic Time Series Models in Python

arXiv 1906.05264 ↗First posted 12 Jun 2019cs.LG stat.MLcited by 45

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Abstract (arXiv)

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.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 9; 2022: 5; 2023: 10; 2024: 8; 2025: 8; 2026: 5.

45 papers.

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  1. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  2. 2021Do We Really Need Deep Learning Models for Time Series Forecasting?cited by 45
  3. 2021ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Modelscited by 42
  4. 2024The Rise of Diffusion Models in Time-Series Forecastingcited by 25
  5. 2022Selective Prediction via Training Dynamicscited by 20
  6. 2025TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysiscited by 18
  7. 2021Variance Reduced Training with Stratified Sampling for Forecasting Modelscited by 13
  8. 2023Deep Learning based Forecasting: a case study from the online fashion industrycited by 12
  9. 2025Zero-Shot Time Series Forecasting with Covariates via In-Context Learningcited by 12
  10. 2026Reverso: Efficient Time Series Foundation Models for Zero-shot Forecastingcited by 11
  11. 2021Deep Autoregressive Models with Spectral Attentioncited by 9
  12. 2025Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Modelscited by 8
33 more
  1. 2021Causal Forecasting:Generalization Bounds for Autoregressive Modelscited by 5
  2. 2023Deep Non-Parametric Time Series Forecastercited by 5
  3. 2026TempusBench: An Evaluation Framework for Time-Series Forecastingcited by 4
  4. 2023DeepTSF: Codeless machine learning operations for time series forecastingcited by 3
  5. 2023XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatilitycited by 3
  6. 2024Data Augmentation for Multivariate Time Series Classification: An Experimental Studycited by 3
  7. 2025time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Modelscited by 3
  8. 2025TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecastingcited by 3
  9. 2023Non-parametric Probabilistic Time Series Forecasting via Innovations Representationcited by 2
  10. 2024Recurrent Interpolants for Probabilistic Time Series Predictioncited by 2
  11. 2021Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture modelscited by 1
  12. 2021Interpretability in Safety-Critical FinancialTrading Systemscited by 1
  13. 2023SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noisecited by 1
  14. 2023Comparative Study of Predicting Stock Index Using Deep Learning Modelscited by 1
  15. 2024Generative Probabilistic Time Series Forecasting and Applications in Grid Operationscited by 1
  16. 2024A Scalable and Transferable Time Series Prediction Framework for Demand Forecastingcited by 1
  17. 2024Evaluating the effectiveness of predicting covariates in LSTM Networks for Time Series Forecastingcited by 1
  18. 2025Measuring Time-Series Dataset Similarity using Wasserstein Distancecited by 1
  19. 2021Optimal Latent Space Forecasting for Large Collections of Short Time Series Using Temporal Matrix Factorizationcited by 0
  20. 2022A Review of Open Source Software Tools for Time Series Analysiscited by 0
  21. 2022Neural Forecasting of the Italian Sovereign Bond Market with Economic Newscited by 0
  22. 2022A Survey of Open Source Automation Tools for Data Science Predictionscited by 0
  23. 2022A Hybrid Statistical-Machine Learning Approach for Analysing Online Customer Behavior: An Empirical Studycited by 0
  24. 2023The DeepCAR Method: Forecasting Time-Series Data That Have Change Pointscited by 0
  25. 2023Making forecasting self-learning and adaptive -- Pilot forecasting rackcited by 0
  26. 2023News Signals: An NLP Library for Text and Time Seriescited by 0
  27. 2024Hyperparameter Tuning MLPs for Probabilistic Time Series Forecastingcited by 0
  28. 2024The State of Lithium-Ion Battery Health Prognostics in the CPS Eracited by 0
  29. 2025ARIMA_PLUS: Large-scale, Accurate, Automatic and Interpretable In-Database Time Series Forecasting and Anomaly Detection in Google BigQuerycited by 0
  30. 2025Forecasting in Offline Reinforcement Learning for Non-stationary Environmentscited by 0
  31. 2026MICA: Multivariate Infini Compressive Attention for Time Series Forecastingcited by 0
  32. 2026Universal Time Series Generation with Neural Controlled Differential Equationscited by 0
  33. 2026FlowTSFM: Turning Encoder Depth into Quantile Transportcited by 0