mlsanityData to 14 Oct 2026

STAEformer: Spatio-Temporal Adaptive Embedding Makes Vanilla Transformer SOTA for Traffic Forecasting

arXiv 2308.10425 ↗First posted 21 Aug 2023cs.LGcited by 12

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Abstract (arXiv)

With the rapid development of the Intelligent Transportation System (ITS), accurate traffic forecasting has emerged as a critical challenge. The key bottleneck lies in capturing the intricate spatio-temporal traffic patterns. In recent years, numerous neural networks with complicated architectures have been proposed to address this issue. However, the advancements in network architectures have encountered diminishing performance gains. In this study, we present a novel component called spatio-temporal adaptive embedding that can yield outstanding results with vanilla transformers. Our proposed Spatio-Temporal Adaptive Embedding transformer (STAEformer) achieves state-of-the-art performance on five real-world traffic forecasting datasets. Further experiments demonstrate that spatio-temporal adaptive embedding plays a crucial role in traffic forecasting by effectively capturing intrinsic spatio-temporal relations and chronological information in traffic time series.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 21 Aug 2023.

Builds on

30 of 44 references.

References · 30

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  3. 2021Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecastingcited by 1,676
  4. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  5. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  6. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  7. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  8. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  9. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  10. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  11. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  12. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
18 more
  1. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  2. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  3. 2020Spatial-Temporal Transformer Networks for Traffic Flow Forecastingcited by 180
  4. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 171
  5. 2023PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Predictioncited by 137
  6. 2022Decoupled Dynamic Spatial-Temporal Graph Neural Network for Traffic Forecastingcited by 123
  7. 2022Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecastingcited by 117
  8. 2022Spatio-Temporal Meta-Graph Learning for Traffic Forecastingcited by 88
  9. 2021DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Predictioncited by 73
  10. 2021Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecastingcited by 67
  11. 2022Towards Spatio-Temporal Aware Traffic Time Series Forecasting--Full Versioncited by 59
  12. 2020Coupled Layer-wise Graph Convolution for Transportation Demand Predictioncited by 54
  13. 2021Historical Inertia: A Neglected but Powerful Baseline for Long Sequence Time-series Forecastingcited by 26
  14. 2021A Multi-view Multi-task Learning Framework for Multi-variate Time Series Forecastingcited by 21
  15. 2021Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecastingcited by 19
  16. 2021Event-Aware Multimodal Mobility Nowcastingcited by 18
  17. 2020Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search Based on Reinforcement Learning and Existing Research Resultscited by 9
  18. 2022Easy Begun is Half Done: Spatial-Temporal Graph Modeling with ST-Curriculum Dropoutcited by 7

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 4.

12 papers.

  1. 2024CCDSReFormer: Traffic Flow Prediction with a Criss-Crossed Dual-Stream Enhanced Rectified Transformer Modelcited by 5
  2. 2024RAM: Replace Attention with MLP for Efficient Multivariate Time Series Forecastingcited by 3
  3. 2024Spatiotemporal Graph Learning with Direct Volumetric Information Passing and Feature Enhancementcited by 1
  4. 2025Efficient Traffic Forecasting on Large-Scale Road Network by Regularized Adaptive Graph Convolutioncited by 1
  5. 2026Sim-MSTNet: sim2real based Multi-task SpatioTemporal Network Traffic Forecastingcited by 1
  6. 2025A Predictive Services Architecture for Efficient Airspace Operationscited by 0
  7. 2025SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Modelscited by 0
  8. 2025FaCTR: Factorized Channel-Temporal Representation Transformers for Efficient Time Series Forecastingcited by 0
  9. 2025A Knowledge-Guided Cross-Modal Feature Fusion Model for Local Traffic Demand Predictioncited by 0
  10. 2026Generalising Travel Time Prediction To Varying Route Choices In Urban Networkscited by 0
  11. 2026More Sensors Only One Field: Rethinking Continual Spatio-Temporal Forecastingcited by 0
  12. 2026AdaST: Adaptive Coupling for Spatial-Temporal Forecastingcited by 0