mlsanityData to 7 Oct 2026

N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

arXiv 1905.10437 ↗First posted 24 May 2019cs.LG stat.MLcited by 744

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Abstract (arXiv)

We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.

Key facts. Cited by 744 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 May 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 58; 2022: 81; 2023: 132; 2024: 145; 2025: 198; 2026: 130.

744 papers.

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  1. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,654
  2. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,200
  3. 2022Diffusion Models: A Comprehensive Survey of Methods and Applicationscited by 1,107
  4. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,030
  5. 2023iTransformer: Inverted Transformers Are Effective for Time Series Forecastingcited by 906
  6. 2024Chronos: Learning the Language of Time Seriescited by 712
  7. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 680
  8. 2023A decoder-only foundation model for time-series forecastingcited by 650
  9. 2023One Fits All:Power General Time Series Analysis by Pretrained LMcited by 615
  10. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 603
  11. 2024Unified Training of Universal Time Series Forecasting Transformerscited by 588
  12. 2023Large Language Models Are Zero-Shot Time Series Forecasterscited by 491
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  1. 2021TS2Vec: Towards Universal Representation of Time Seriescited by 463
  2. 2024MOMENT: A Family of Open Time-series Foundation Modelscited by 453
  3. 2022Non-stationary Transformers: Exploring the Stationarity in Time Series Forecastingcited by 385
  4. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 335
  5. 2024TimeMixer: Decomposable Multiscale Mixing for Time Series Forecastingcited by 333
  6. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 332
  7. 2023Long-term Forecasting with TiDE: Time-series Dense Encodercited by 327
  8. 2023ClimaX: A foundation model for weather and climatecited by 325
  9. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 298
  10. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Expertscited by 280
  11. 2021Monash Time Series Forecasting Archivecited by 256
  12. 2024TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methodscited by 242
  13. 2024TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variablescited by 241
  14. 2023TSMixer: An All-MLP Architecture for Time Series Forecastingcited by 232
  15. 2024Deep Time Series Models: A Comprehensive Survey and Benchmarkcited by 191
  16. 2024GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluationcited by 188
  17. 2023FITS: Modeling Time Series with 10k Parameterscited by 185
  18. 2023TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecastingcited by 170
  19. 2023Frequency-domain MLPs are More Effective Learners in Time Series Forecastingcited by 170
  20. 2025Sundial: A Family of Highly Capable Time Series Foundation Modelscited by 168
  21. 2023TEST: Text Prototype Aligned Embedding to Activate LLM's Ability for Time Seriescited by 165
  22. 2022Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structurescited by 158
  23. 2025Chronos-2: From Univariate to Universal Forecastingcited by 158
  24. 2023TimeGPT-1cited by 149
  25. 2022FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecastingcited by 147
  26. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  27. 2022ETSformer: Exponential Smoothing Transformers for Time-series Forecastingcited by 146
  28. 2022CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecastingcited by 143
  29. 2023Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Surveycited by 140
  30. 2021TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generationcited by 139
  31. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 122
  32. 2024Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Seriescited by 120
  33. 2023Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysiscited by 113
  34. 2024TimeMixer++: A General Time Series Pattern Machine for Universal Predictive Analysiscited by 113
  35. 2023Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictorscited by 110
  36. 2023Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlookcited by 109
  37. 2025TiRex: Zero-Shot Forecasting Across Long and Short Horizons with Enhanced In-Context Learningcited by 107
  38. 2024CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuningcited by 105

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