# AutoGluon-TimeSeries: AutoML for Probabilistic Time Series Forecasting

arXiv: https://arxiv.org/abs/2308.05566 · HTML: https://mlsanity.com/paper/2308.05566/

## 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 (most cited first, 20 of 20)

- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [A Multi-Horizon Quantile Recurrent Forecaster](https://mlsanity.com/paper/1711.11053/) (2017)
- [Deep Learning for Time Series Forecasting: Tutorial and Literature Survey](https://mlsanity.com/paper/2004.10240/) (2020)
- [Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey](https://mlsanity.com/paper/2104.00950/) (2021)
- [AMLB: an AutoML Benchmark](https://mlsanity.com/paper/2207.12560/) (2022)
- [Criteria for Classifying Forecasting Methods](https://mlsanity.com/paper/2212.03523/) (2022)
- [AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks](https://mlsanity.com/paper/2306.08107/) (2023)
- [Forecast combinations: an over 50-year review](https://mlsanity.com/paper/2205.04216/) (2022)
- [Review of automated time series forecasting pipelines](https://mlsanity.com/paper/2202.01712/) (2022)
- [AutoAI-TS: AutoAI for Time Series Forecasting](https://mlsanity.com/paper/2102.12347/) (2021)
- [Efficient Automated Deep Learning for Time Series Forecasting](https://mlsanity.com/paper/2205.05511/) (2022)
- [A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning](https://mlsanity.com/paper/2104.11475/) (2021)
- [Multi-Objective Model Selection for Time Series Forecasting](https://mlsanity.com/paper/2202.08485/) (2022)
- [Improving Neural Networks for Time Series Forecasting using Data Augmentation and AutoML](https://mlsanity.com/paper/2103.01992/) (2021)

## Built on by (most cited first, 30 of 36)

- [Chronos-2: From Univariate to Universal Forecasting](https://mlsanity.com/paper/2510.15821/) (2025)
- [ForecastPFN: Synthetically-Trained Zero-Shot Forecasting](https://mlsanity.com/paper/2311.01933/) (2023)
- [AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML](https://mlsanity.com/paper/2410.02958/) (2024)
- [fev-bench: A Realistic Benchmark for Time Series Forecasting](https://mlsanity.com/paper/2509.26468/) (2025)
- [From Tables to Time: Extending TabPFN-v2 to Time Series Forecasting](https://mlsanity.com/paper/2501.02945/) (2025)
- [Flow Matching with Gaussian Process Priors for Probabilistic Time Series Forecasting](https://mlsanity.com/paper/2410.03024/) (2024)
- [AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models](https://mlsanity.com/paper/2404.16233/) (2024)
- [TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis](https://mlsanity.com/paper/2410.04047/) (2024)
- [Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models](https://mlsanity.com/paper/2410.09385/) (2024)
- [Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting](https://mlsanity.com/paper/2505.18442/) (2025)
- [TimeRecipe: A Time-Series Forecasting Recipe via Benchmarking Module Level Effectiveness](https://mlsanity.com/paper/2506.06482/) (2025)
- [TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning](https://mlsanity.com/paper/2606.01498/) (2026)
- [Probabilistic NDVI Forecasting from Sparse Satellite Time Series and Weather Covariates](https://mlsanity.com/paper/2602.17683/) (2026)
- [Post-Training in Time Series Foundation Models: A Unifying Framework](https://mlsanity.com/paper/2607.20002/) (2026)
- [Automatic selection of the best neural architecture for time series forecasting](https://mlsanity.com/paper/2501.12215/) (2025)
- [Measuring Time Series Forecast Stability for Demand Planning](https://mlsanity.com/paper/2508.10063/) (2025)
- [Enhancing Transformer-Based Foundation Models for Time Series Forecasting via Bagging, Boosting and Statistical Ensembles](https://mlsanity.com/paper/2508.16641/) (2025)
- [Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting](https://mlsanity.com/paper/2510.06419/) (2025)
- [MOSAIC: Modular Orchestration for Structured Agentic Intelligence and Composition](https://mlsanity.com/paper/2606.00708/) (2026)
- [Foundation Models and Fine-Tuning: Toward a New Generation of Models for Time Series Forecasting](https://mlsanity.com/paper/2607.23146/) (2026)
- [auto-sktime: Automated Time Series Forecasting](https://mlsanity.com/paper/2312.08528/) (2023)
- [Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity](https://mlsanity.com/paper/2510.18037/) (2025)
- [Can time series forecasting be automated? A benchmark and analysis](https://mlsanity.com/paper/2407.16445/) (2024)
- [AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability](https://mlsanity.com/paper/2412.00419/) (2024)
- [TSGym: Design Choices for Deep Multivariate Time-Series Forecasting](https://mlsanity.com/paper/2509.17063/) (2025)
- [An AutoML Framework using AutoGluonTS for Forecasting Seasonal Extreme Temperatures](https://mlsanity.com/paper/2509.17734/) (2025)
- [Automated Machine Learning for Unsupervised Tabular Tasks](https://mlsanity.com/paper/2510.07569/) (2025)
- [QKCV Attention: Enhancing Time Series Forecasting with Static Categorical Embeddings for Both Lightweight and Pre-trained Foundation Models](https://mlsanity.com/paper/2510.20222/) (2025)
- [Multi-layer Stack Ensembles for Time Series Forecasting](https://mlsanity.com/paper/2511.15350/) (2025)
- [msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models](https://mlsanity.com/paper/2603.16497/) (2026)

## 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.