mlsanityData to 14 Oct 2026

AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

arXiv 2308.05566 ↗First posted 10 Aug 2023cs.LGcited by 36

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Abstract (arXiv)

We introduce AutoGluon-TimeSeries - an open-source AutoML library for probabilistic time series forecasting. Focused on ease of use and robustness, AutoGluon-TimeSeries enables users to generate accurate point and quantile forecasts with just 3 lines of Python code. Built on the design philosophy of AutoGluon, AutoGluon-TimeSeries leverages ensembles of diverse forecasting models to deliver high accuracy within a short training time. AutoGluon-TimeSeries combines both conventional statistical models, machine-learning based forecasting approaches, and ensembling techniques. In our evaluation on 29 benchmark datasets, AutoGluon-TimeSeries demonstrates strong empirical performance, outperforming a range of forecasting methods in terms of both point and quantile forecast accuracy, and often even improving upon the best-in-hindsight combination of prior methods.

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Builds on 20 of its 69 references that are in the map. First posted on arXiv 10 Aug 2023.

Builds on

20 of 69 references.

References · 20

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  2. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  3. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  4. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  5. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 753
  6. 2020AutoGluon-Tabular: Robust and Accurate AutoML for Structured Datacited by 383
  7. 2021Monash Time Series Forecasting Archivecited by 267
  8. 2017A Multi-Horizon Quantile Recurrent Forecastercited by 211
  9. 2020Deep Learning for Time Series Forecasting: Tutorial and Literature Surveycited by 117
  10. 2021Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Surveycited by 72
  11. 2022AMLB: an AutoML Benchmarkcited by 55
  12. 2022Criteria for Classifying Forecasting Methodscited by 48
8 more
  1. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 35
  2. 2022Forecast combinations: an over 50-year reviewcited by 34
  3. 2022Review of automated time series forecasting pipelinescited by 18
  4. 2021AutoAI-TS: AutoAI for Time Series Forecastingcited by 15
  5. 2022Efficient Automated Deep Learning for Time Series Forecastingcited by 14
  6. 2021A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learningcited by 10
  7. 2022Multi-Objective Model Selection for Time Series Forecastingcited by 8
  8. 2021Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoMLcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 15; 2026: 12.

36 papers.

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  1. 2025Chronos-2: From Univariate to Universal Forecastingcited by 194
  2. 2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 104
  3. 2024AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLcited by 87
  4. 2025fev-bench: A Realistic Benchmark for Time Series Forecastingcited by 64
  5. 2025From Tables to Time: Extending TabPFN-v2 to Time Series Forecastingcited by 60
  6. 2024Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecastingcited by 43
  7. 2024AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Modelscited by 30
  8. 2024TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysiscited by 29
  9. 2024Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Modelscited by 17
  10. 2025Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecastingcited by 12
  11. 2025TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectivenesscited by 7
  12. 2026TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoningcited by 5
24 more
  1. 2026Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariatescited by 3
  2. 2026Post-Training in Time Series Foundation Models: A Unifying Frameworkcited by 3
  3. 2025Automatic selection of the best neural architecture for time series forecastingcited by 2
  4. 2025Measuring Time Series Forecast Stability for Demand Planningcited by 2
  5. 2025Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensemblescited by 2
  6. 2025Test-Time Efficient Pretrained Model Portfolios for Time Series Forecastingcited by 2
  7. 2026MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Compositioncited by 2
  8. 2026Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecastingcited by 2
  9. 2023auto-sktime: Automated Time Series Forecastingcited by 1
  10. 2025Benchmarking Probabilistic Time Series Forecasting Models on Neural Activitycited by 1
  11. 2024Can time series forecasting be automated? A benchmark and analysiscited by 0
  12. 2024AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainabilitycited by 0
  13. 2025TSGym: Design Choices for Deep Multivariate Time-Series Forecastingcited by 0
  14. 2025An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperaturescited by 0
  15. 2025Automated Machine Learning for Unsupervised Tabular Taskscited by 0
  16. 2025QKCV Attention: Enhancing Time Series Forecasting with Static Categorical Embeddings for Both Lightweight and Pre-trained Foundation Modelscited by 0
  17. 2025Multi-layer Stack Ensembles for Time Series Forecastingcited by 0
  18. 2026msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Modelscited by 0
  19. 2026PaP-NF: Probabilistic Long-Term Time Series Forecasting via Prefix-as-Prompt Reprogramming and Normalizing Flowscited by 0
  20. 2026FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecastingcited by 0
  21. 2026VegSim: A Geospatial World Model for Scenario-Conditioned Vegetation Simulationcited by 0
  22. 2026Hopformer: Homogeneity-Pursuit Transformer for Time Series Forecastingcited by 0
  23. 2026Systematic Evaluation of TabPFN-TS and Chronos-2 for Zero-Shot Heat Load Forecasting in District Heating Networkscited by 0
  24. 2026The Nixtlaverse: An Open-Source Ecosystem for Forecastingcited by 0