Data to 7 Oct 2026

DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

arXiv 1704.04110 ↗First posted 13 Apr 2017cs.AI cs.LG stat.MLcited by 1,004

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Cited by 25 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Probabilistic forecasting, i.e. estimating the probability distribution of a time series' future given its past, is a key enabler for optimizing business processes. In retail businesses, for example, forecasting demand is crucial for having the right inventory available at the right time at the right place. In this paper we propose DeepAR, a methodology for producing accurate probabilistic forecasts, based on training an auto regressive recurrent network model on a large number of related time series. We demonstrate how by applying deep learning techniques to forecasting, one can overcome many of the challenges faced by widely-used classical approaches to the problem. We show through extensive empirical evaluation on several real-world forecasting data sets accuracy improvements of around 15% compared to state-of-the-art methods.

Key facts. Cited by 1,004 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Apr 2017.

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Citing papers per year: 2021: 107; 2022: 137; 2023: 166; 2024: 189; 2025: 214; 2026: 191.

1,004 papers.

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2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 1,981
2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
2023A decoder-only foundation model for time-series forecastingcited by 650
2024Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusioncited by 634
2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385

38 more

2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 327
2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
2021Monash Time Series Forecasting Archivecited by 256
2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 188
2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
2025Sundial: A Family of Highly Capable Time Series Foundation Modelscited by 168
2025Chronos-2: From Univariate to Universal Forecastingcited by 158
2023TimeGPT-1cited by 149
2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
2023FourierGNN: Rethinking Multivariate Time Series Forecasting from a Pure Graph Perspectivecited by 126
2021AdaRNN: Adaptive Learning and Forecasting of Time Seriescited by 120
2024Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Seriescited by 120
2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 110
2025TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learningcited by 107
2023DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detectioncited by 103
2023Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecastingcited by 102
2023ForecastPFN: Synthetically-Trained Zero-Shot Forecastingcited by 102
2024CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patternscited by 102
2023The Capacity and Robustness Trade-off: Revisiting the Channel Independent Strategy for Multivariate Time Series Forecastingcited by 100
2021Neural Flows: Efficient Alternative to Neural ODEscited by 99
2024ChatTime: A Unified Multimodal Time Series Foundation Model Bridging Numerical and Textual Datacited by 92
2023Dish-TS: A General Paradigm for Alleviating Distribution Shift in Time Series Forecastingcited by 91
2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 90
2023Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglementcited by 89
2023Predict, Refine, Synthesize: Self-Guiding Diffusion Models for Probabilistic Time Series Forecastingcited by 86
2024Timer-XL: Long-Context Transformers for Unified Time Series Forecastingcited by 83
2023DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Modelscited by 81
2024FreDF: Learning to Forecast in the Frequency Domaincited by 77

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