mlsanityData to 14 Oct 2026

A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification

arXiv 2410.22377 ↗First posted 29 Oct 2024cs.LG cs.AI physics.data-ancited by 9

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Abstract (arXiv)

In recent years, spatio-temporal graph neural networks (GNNs) have attracted considerable interest in the field of time series analysis, due to their ability to capture, at once, dependencies among variables and across time points. The objective of this systematic literature review is hence to provide a comprehensive overview of the various modeling approaches and application domains of GNNs for time series classification and forecasting. A database search was conducted, and 366 papers were selected for a detailed examination of the current state-of-the-art in the field. This examination is intended to offer to the reader a comprehensive review of proposed models, links to related source code, available datasets, benchmark models, and fitting results. All this information is hoped to assist researchers in their studies. To the best of our knowledge, this is the first and broadest systematic literature review presenting a detailed comparison of results from current spatio-temporal GNN models applied to different domains. In its final part, this review discusses current limitations and challenges in the application of spatio-temporal GNNs, such as comparability, reproducibility, explainability, poor information capacity, and scalability. This paper is complemented by a GitHub repository at https://github.com/FlaGer99/SLR-Spatio-Temporal-GNN.git providing additional interactive tools to further explore the presented findings.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 146 of its 505 references that are in the map. First posted on arXiv 29 Oct 2024.

Builds on

146 of 505 references.

References · 146

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  4. 2017Graph Attention Networkscited by 8,923
  5. 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  7. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,919
  8. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,434
  9. 2020Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecastingcited by 2,169
  10. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  11. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  12. 2016node2vec: Scalable Feature Learning for Networkscited by 1,918
38 more
  1. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  2. 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
  3. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,424
  4. 2022FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecastingcited by 1,210
  5. 2017Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecastingcited by 1,136
  6. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,013
  7. 2017Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networkscited by 879
  8. 2021How Attentive are Graph Attention Networks?cited by 835
  9. 2019Graph WaveNet for Deep Spatial-Temporal Graph Modelingcited by 829
  10. 2020Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural Networkscited by 566
  11. 2020Adaptive Graph Convolutional Recurrent Network for Traffic Forecastingcited by 530
  12. 2020A Transformer-based Framework for Multivariate Time Series Representation Learningcited by 453
  13. 2019GMAN: A Graph Multi-Attention Network for Traffic Predictioncited by 434
  14. 2018T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Predictioncited by 401
  15. 2019InceptionTime: Finding AlexNet for Time Series Classificationcited by 387
  16. 2021Time-Series Representation Learning via Temporal and Contextual Contrastingcited by 345
  17. 2019ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernelscited by 339
  18. 2021SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interactioncited by 338
  19. 2021Autoregressive Denoising Diffusion Models for Multivariate Probabilistic Time Series Forecastingcited by 337
  20. 2016Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Predictioncited by 333
  21. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  22. 2021Spectral Temporal Graph Neural Network for Multivariate Time-series Forecastingcited by 299
  23. 2021Graph Neural Network for Traffic Forecasting: A Surveycited by 261
  24. 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Predictioncited by 245
  25. 2024Foundation Models for Time Series Analysis: A Tutorial and Surveycited by 239
  26. 2021Multi-Time Attention Networks for Irregularly Sampled Time Seriescited by 238
  27. 2022TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Datacited by 214
  28. 2020Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecastingcited by 213
  29. 2021Spatial-Temporal Graph ODE Networks for Traffic Flow Forecastingcited by 210
  30. 2020MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classificationcited by 190
  31. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
  32. 2021Discrete Graph Structure Learning for Forecasting Multiple Time Seriescited by 171
  33. 2024Are Language Models Actually Useful for Time Series Forecasting?cited by 168
  34. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 167
  35. 2018GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphscited by 165
  36. 2018Deep Multi-View Spatial-Temporal Network for Taxi Demand Predictioncited by 162
  37. 2017DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEGcited by 157
  38. 2021Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solutioncited by 157

50 most cited of 146 All 146 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 5.

9 papers.

  1. 2025Diffusion Models for Time Series Forecasting: A Surveycited by 11
  2. 2026Empowering Power Outage Prediction with Spatially Aware Hybrid Graph Neural Networks and Contrastive Learningcited by 1
  3. 2024STDHL: Spatio-Temporal Dynamic Hypergraph Learning for Wind Power Forecastingcited by 0
  4. 2025On the use of Graphs for Satellite Image Time Seriescited by 0
  5. 2025SVDformer: Direction-Aware Spectral Graph Embedding Learning via SVD and Transformercited by 0
  6. 2026Interpretability in Deep Time Series Models Demands Semantic Alignmentcited by 0
  7. 2026ST-ResGAT: Explainable Spatio-Temporal Graph Neural Network for Road Condition Prediction and Priority-Driven Maintenancecited by 0
  8. 2026ClouDens: Operational Context-Aware Anomaly Detection for Large-scale Cloud System Monitoringcited by 0
  9. 2026A Critical Audit of Spatiotemporal Forecasting Benchmark Datasets and Modelscited by 0