# GluonTS: Probabilistic Time Series Models in Python

arXiv: https://arxiv.org/abs/1906.05264 · HTML: https://mlsanity.com/paper/1906.05264/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2019.

## Built on by (most cited first, 30 of 46)

- [PSA-GAN: Progressive Self Attention GANs for Synthetic Time Series](https://mlsanity.com/paper/2108.00981/) (2021)
- [Do We Really Need Deep Learning Models for Time Series Forecasting?](https://mlsanity.com/paper/2101.02118/) (2021)
- [ScoreGrad: Multivariate Probabilistic Time Series Forecasting with Continuous Energy-based Generative Models](https://mlsanity.com/paper/2106.10121/) (2021)
- [The Rise of Diffusion Models in Time-Series Forecasting](https://mlsanity.com/paper/2401.03006/) (2024)
- [TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis](https://mlsanity.com/paper/2510.01538/) (2025)
- [Selective Prediction via Training Dynamics](https://mlsanity.com/paper/2205.13532/) (2022)
- [Variance Reduced Training with Stratified Sampling for Forecasting Models](https://mlsanity.com/paper/2103.02062/) (2021)
- [Reverso: Efficient Time Series Foundation Models for Zero-shot Forecasting](https://mlsanity.com/paper/2602.17634/) (2026)
- [Deep Learning based Forecasting: a case study from the online fashion industry](https://mlsanity.com/paper/2305.14406/) (2023)
- [Zero-Shot Time Series Forecasting with Covariates via In-Context Learning](https://mlsanity.com/paper/2506.03128/) (2025)
- [Deep Autoregressive Models with Spectral Attention](https://mlsanity.com/paper/2107.05984/) (2021)
- [Dynamical Diffusion: Learning Temporal Dynamics with Diffusion Models](https://mlsanity.com/paper/2503.00951/) (2025)
- [Causal Forecasting:Generalization Bounds for Autoregressive Models](https://mlsanity.com/paper/2111.09831/) (2021)
- [Deep Non-Parametric Time Series Forecaster](https://mlsanity.com/paper/2312.14657/) (2023)
- [time2time: Causal Intervention in Hidden States to Simulate Rare Events in Time Series Foundation Models](https://mlsanity.com/paper/2509.05801/) (2025)
- [TempusBench: An Evaluation Framework for Time-Series Forecasting](https://mlsanity.com/paper/2604.11529/) (2026)
- [DeepTSF: Codeless machine learning operations for time series forecasting](https://mlsanity.com/paper/2308.00709/) (2023)
- [XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility](https://mlsanity.com/paper/2310.09847/) (2023)
- [Data Augmentation for Multivariate Time Series Classification: An Experimental Study](https://mlsanity.com/paper/2406.06518/) (2024)
- [TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting](https://mlsanity.com/paper/2511.18539/) (2025)
- [Non-parametric Probabilistic Time Series Forecasting via Innovations Representation](https://mlsanity.com/paper/2306.03782/) (2023)
- [Recurrent Interpolants for Probabilistic Time Series Prediction](https://mlsanity.com/paper/2409.11684/) (2024)
- [Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models](https://mlsanity.com/paper/2106.03023/) (2021)
- [Interpretability in Safety-Critical FinancialTrading Systems](https://mlsanity.com/paper/2109.15112/) (2021)
- [SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise](https://mlsanity.com/paper/2305.16491/) (2023)
- [Comparative Study of Predicting Stock Index Using Deep Learning Models](https://mlsanity.com/paper/2306.13931/) (2023)
- [Generative Probabilistic Time Series Forecasting and Applications in Grid Operations](https://mlsanity.com/paper/2402.13870/) (2024)
- [A Scalable and Transferable Time Series Prediction Framework for Demand Forecasting](https://mlsanity.com/paper/2402.19402/) (2024)
- [Evaluating the effectiveness of predicting covariates in LSTM Networks for Time Series Forecasting](https://mlsanity.com/paper/2404.18553/) (2024)
- [Measuring Time-Series Dataset Similarity using Wasserstein Distance](https://mlsanity.com/paper/2507.22189/) (2025)

## 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.