Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey (2303.14483v3)

Published 25 Mar 2023 in cs.LG

Abstract: With recent advances in sensing technologies, a myriad of spatio-temporal data has been generated and recorded in smart cities. Forecasting the evolution patterns of spatio-temporal data is an important yet demanding aspect of urban computing, which can enhance intelligent management decisions in various fields, including transportation, environment, climate, public safety, healthcare, and others. Traditional statistical and deep learning methods struggle to capture complex correlations in urban spatio-temporal data. To this end, Spatio-Temporal Graph Neural Networks (STGNN) have been proposed, achieving great promise in recent years. STGNNs enable the extraction of complex spatio-temporal dependencies by integrating graph neural networks (GNNs) and various temporal learning methods. In this manuscript, we provide a comprehensive survey on recent progress on STGNN technologies for predictive learning in urban computing. Firstly, we provide a brief introduction to the construction methods of spatio-temporal graph data and the prevalent deep-learning architectures used in STGNNs. We then sort out the primary application domains and specific predictive learning tasks based on existing literature. Afterward, we scrutinize the design of STGNNs and their combination with some advanced technologies in recent years. Finally, we conclude the limitations of existing research and suggest potential directions for future work.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (231)
  1. Y. Zheng, L. Capra, O. Wolfson, and H. Yang, “Urban computing: concepts, methodologies, and applications,” ACM TIST, vol. 5, no. 3, pp. 1–55, 2014.
  2. X. Wang, L. Li, Y. Yuan, P. Ye, and F.-Y. Wang, “Acp-based social computing and parallel intelligence: Societies 5.0 and beyond,” CAAI Transactions on Intelligence Technology, vol. 1, no. 4, pp. 377–393, 2016.
  3. S. Wang, J. Cao, and P. Yu, “Deep learning for spatio-temporal data mining: A survey,” IEEE TKDE, 2020.
  4. H. Drucker, C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik, “Support vector regression machines,” in NeurIPS, vol. 9, 1996.
  5. L. Breiman, “Random forests,” Machine learning, vol. 45, pp. 5–32, 2001.
  6. A. Natekin and A. Knoll, “Gradient boosting machines, a tutorial,” Frontiers in neurorobotics, vol. 7, p. 21, 2013.
  7. J. Gu, Z. Wang, J. Kuen, L. Ma, A. Shahroudy, B. Shuai, T. Liu, X. Wang, G. Wang, J. Cai, et al., “Recent advances in convolutional neural networks,” Pattern recognition, vol. 77, pp. 354–377, 2018.
  8. Y. Yu, X. Si, C. Hu, and J. Zhang, “A review of recurrent neural networks: Lstm cells and network architectures,” Neural computation, vol. 31, no. 7, pp. 1235–1270, 2019.
  9. X. Shi, Z. Chen, H. Wang, D.-Y. Yeung, W.-K. Wong, and W.-c. Woo, “Convolutional lstm network: A machine learning approach for precipitation nowcasting,” in NeurIPS, vol. 28, 2015.
  10. Y. Wang, M. Long, J. Wang, Z. Gao, and P. S. Yu, “Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms,” in NeurIPS, vol. 30, 2017.
  11. T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907, 2016.
  12. J. Ye, J. Zhao, K. Ye, and C. Xu, “How to build a graph-based deep learning architecture in traffic domain: A survey,” IEEE TITS, vol. 23, no. 5, pp. 3904–3924, 2020.
  13. K.-H. N. Bui, J. Cho, and H. Yi, “Spatial-temporal graph neural network for traffic forecasting: An overview and open research issues,” Applied Intelligence, vol. 52, no. 3, pp. 2763–2774, 2022.
  14. W. Jiang and J. Luo, “Graph neural network for traffic forecasting: A survey,” Expert Systems with Applications, p. 117921, 2022.
  15. Z. A. Sahili and M. Awad, “Spatio-temporal graph neural networks: A survey,” arXiv preprint arXiv:2301.10569, 2023.
  16. Y. Li, D. Yu, Z. Liu, M. Zhang, X. Gong, and L. Zhao, “Graph neural network for spatiotemporal data: methods and applications,” arXiv preprint arXiv:2306.00012, 2023.
  17. S. Guo, Y. Lin, H. Wan, X. Li, and G. Cong, “Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,” IEEE TKDE, vol. 34, no. 11, pp. 5415–5428, 2021.
  18. X. Wang, Y. Ma, Y. Wang, W. Jin, X. Wang, J. Tang, C. Jia, and J. Yu, “Traffic flow prediction via spatial temporal graph neural network,” in WWW, 2020, pp. 1082–1092.
  19. D. Chai, L. Wang, and Q. Yang, “Bike flow prediction with multi-graph convolutional networks,” in SIGSPATIAL, 2018, pp. 397–400.
  20. B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting,” arXiv preprint arXiv:1709.04875, 2017.
  21. G. Jin, Z. Xi, H. Sha, Y. Feng, and J. Huang, “Deep multi-view graph-based network for citywide ride-hailing demand prediction,” Neurocomputing, vol. 510, pp. 79–94, 2022.
  22. L. Liu, J. Chen, H. Wu, J. Zhen, G. Li, and L. Lin, “Physical-virtual collaboration modeling for intra-and inter-station metro ridership prediction,” IEEE TITS, vol. 23, no. 4, pp. 3377–3391, 2020.
  23. F. Li, H. Yan, G. Jin, Y. Liu, Y. Li, and D. Jin, “Automated spatio-temporal synchronous modeling with multiple graphs for traffic prediction,” in CIKM, 2022, pp. 1084–1093.
  24. H. Shi, Q. Yao, Q. Guo, Y. Li, L. Zhang, J. Ye, Y. Li, and Y. Liu, “Predicting origin-destination flow via multi-perspective graph convolutional network,” in ICDE.   IEEE, 2020, pp. 1818–1821.
  25. X. Geng, Y. Li, L. Wang, L. Zhang, Q. Yang, J. Ye, and Y. Liu, “Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting,” in AAAI, vol. 33, no. 01, 2019, pp. 3656–3663.
  26. L. Zhao, Y. Song, C. Zhang, Y. Liu, P. Wang, T. Lin, M. Deng, and H. Li, “T-gcn: A temporal graph convolutional network for traffic prediction,” IEEE TITS, vol. 21, no. 9, pp. 3848–3858, 2019.
  27. J. Ji, J. Wang, Z. Jiang, J. Jiang, and H. Zhang, “Stden: Towards physics-guided neural networks for traffic flow prediction,” in AAAI, vol. 36, no. 4, 2022, pp. 4048–4056.
  28. Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” arXiv preprint arXiv:1707.01926, 2017.
  29. Z. Zhang, M. Li, X. Lin, Y. Wang, and F. He, “Multistep speed prediction on traffic networks: A deep learning approach considering spatio-temporal dependencies,” TRC, vol. 105, pp. 297–322, 2019.
  30. Y. Wang, H. Yin, T. Chen, C. Liu, B. Wang, T. Wo, and J. Xu, “Gallat: A spatiotemporal graph attention network for passenger demand prediction,” in ICDE.   IEEE, 2021, pp. 2129–2134.
  31. X. Zhang, C. Huang, Y. Xu, L. Xia, P. Dai, L. Bo, J. Zhang, and Y. Zheng, “Traffic flow forecasting with spatial-temporal graph diffusion network,” in AAAI, vol. 35, no. 17, 2021, pp. 15 008–15 015.
  32. J. Sun, J. Zhang, Q. Li, X. Yi, Y. Liang, and Y. Zheng, “Predicting citywide crowd flows in irregular regions using multi-view graph convolutional networks,” IEEE TKDE, vol. 34, no. 5, pp. 2348–2359, 2020.
  33. X. Zhang, C. Huang, Y. Xu, and L. Xia, “Spatial-temporal convolutional graph attention networks for citywide traffic flow forecasting,” in CIKM, 2020, pp. 1853–1862.
  34. G. Jin, Y. Cui, L. Zeng, H. Tang, Y. Feng, and J. Huang, “Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network,” TRC, vol. 117, p. 102665, 2020.
  35. J. Ye, L. Sun, B. Du, Y. Fu, and H. Xiong, “Coupled layer-wise graph convolution for transportation demand prediction,” in AAAI, vol. 35, no. 5, 2021, pp. 4617–4625.
  36. Y. Wang, H. Yin, H. Chen, T. Wo, J. Xu, and K. Zheng, “Origin-destination matrix prediction via graph convolution: a new perspective of passenger demand modeling,” in SIGKDD, 2019, pp. 1227–1235.
  37. B. Huang, K. Ruan, W. Yu, J. Xiao, R. Xie, and J. Huang, “Odformer: Spatial-temporal transformers for long sequence origin-destination matrix forecasting against cross application scenario,” Expert Systems with Applications, p. 119835, 2023.
  38. J. Hu, B. Yang, C. Guo, C. S. Jensen, and H. Xiong, “Stochastic origin-destination matrix forecasting using dual-stage graph convolutional, recurrent neural networks,” in ICDE.   IEEE, 2020, pp. 1417–1428.
  39. Z. Dapeng and F. Xiao, “Dynamic auto-structuring graph neural network: a joint learning framework for origin-destination demand prediction,” IEEE TKDE, 2021.
  40. L. Liu, Y. Zhu, G. Li, Z. Wu, L. Bai, and L. Lin, “Online metro origin-destination prediction via heterogeneous information aggregation,” IEEE TPAMI, 2022.
  41. Y. Wang, H. Yin, T. Chen, C. Liu, B. Wang, T. Wo, and J. Xu, “Passenger mobility prediction via representation learning for dynamic directed and weighted graphs,” TIST, vol. 13, no. 1, pp. 1–25, 2021.
  42. B. Wang, Y. Lin, S. Guo, and H. Wan, “Gsnet: learning spatial-temporal correlations from geographical and semantic aspects for traffic accident risk forecasting,” in AAAI, vol. 35, no. 5, 2021, pp. 4402–4409.
  43. L. Yu, B. Du, X. Hu, L. Sun, L. Han, and W. Lv, “Deep spatio-temporal graph convolutional network for traffic accident prediction,” Neurocomputing, vol. 423, pp. 135–147, 2021.
  44. Z. Wang, R. Jiang, H. Xue, F. D. Salim, X. Song, and R. Shibasaki, “Event-aware multimodal mobility nowcasting,” in AAAI, vol. 36, no. 4, 2022, pp. 4228–4236.
  45. Z. Zhou, Y. Wang, X. Xie, L. Chen, and H. Liu, “Riskoracle: a minute-level citywide traffic accident forecasting framework,” in AAAI, vol. 34, no. 01, 2020, pp. 1258–1265.
  46. G. Jin, L. Liu, F. Li, and J. Huang, “Spatio-temporal graph neural point process for traffic congestion event prediction,” in AAAI, vol. 37, no. 12, 2023, pp. 14 268–14 276.
  47. X. Fang, J. Huang, F. Wang, L. Zeng, H. Liang, and H. Wang, “Constgat: Contextual spatial-temporal graph attention network for travel time estimation at baidu maps,” in SIGKDD, 2020, pp. 2697–2705.
  48. J. Huang, Z. Huang, X. Fang, S. Feng, X. Chen, J. Liu, H. Yuan, and H. Wang, “Dueta: Traffic congestion propagation pattern modeling via efficient graph learning for eta prediction at baidu maps,” in CIKM, 2022, pp. 3172–3181.
  49. A. Derrow-Pinion, J. She, D. Wong, O. Lange, T. Hester, L. Perez, M. Nunkesser, S. Lee, X. Guo, B. Wiltshire, et al., “Eta prediction with graph neural networks in google maps,” in CIKM, 2021, pp. 3767–3776.
  50. K. Fu, F. Meng, J. Ye, and Z. Wang, “Compacteta: A fast inference system for travel time prediction,” in SIGKDD, 2020, pp. 3337–3345.
  51. Z. Wu, D. Zheng, S. Pan, Q. Gan, G. Long, and G. Karypis, “Traversenet: Unifying space and time in message passing for traffic forecasting,” IEEE TNNLS, 2022.
  52. M. Li and Z. Zhu, “Spatial-temporal fusion graph neural networks for traffic flow forecasting,” in AAAI, vol. 35, no. 5, 2021, pp. 4189–4196.
  53. C. Song, Y. Lin, S. Guo, and H. Wan, “Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,” in AAAI, vol. 34, no. 01, 2020, pp. 914–921.
  54. G. Jin, F. Li, J. Zhang, M. Wang, and J. Huang, “Automated dilated spatio-temporal synchronous graph modeling for traffic prediction,” IEEE TITS, 2022.
  55. C. Zheng, X. Fan, C. Wang, and J. Qi, “Gman: A graph multi-attention network for traffic prediction,” in AAAI, vol. 34, no. 01, 2020, pp. 1234–1241.
  56. W. Chen, L. Chen, Y. Xie, W. Cao, Y. Gao, and X. Feng, “Multi-range attentive bicomponent graph convolutional network for traffic forecasting,” in AAAI, vol. 34, no. 04, 2020, pp. 3529–3536.
  57. Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” arXiv preprint arXiv:1906.00121, 2019.
  58. F. Li, J. Feng, H. Yan, G. Jin, F. Yang, F. Sun, D. Jin, and Y. Li, “Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution,” ACM TKDD, 2021.
  59. L. Han, B. Du, L. Sun, Y. Fu, Y. Lv, and H. Xiong, “Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting,” in SIGKDD, 2021, pp. 547–555.
  60. D. Liu, J. Wang, S. Shang, and P. Han, “Msdr: Multi-step dependency relation networks for spatial temporal forecasting,” in SIGKDD, 2022, pp. 1042–1050.
  61. G. Li, S. Zhong, X. Deng, L. Xiang, S.-H. G. Chan, R. Li, Y. Liu, M. Zhang, C.-C. Hung, and W.-C. Peng, “A lightweight and accurate spatial-temporal transformer for traffic forecasting,” IEEE TKDE, 2022.
  62. Q. Xie, T. Guo, Y. Chen, Y. Xiao, X. Wang, and B. Y. Zhao, “Deep graph convolutional networks for incident-driven traffic speed prediction,” in CIKM, 2020, pp. 1665–1674.
  63. L. Bai, L. Yao, S. S. Kanhere, X. Wang, W. Liu, and Z. Yang, “Spatio-temporal graph convolutional and recurrent networks for citywide passenger demand prediction,” in CIKM, 2019, pp. 2293–2296.
  64. A. Ali, Y. Zhu, and M. Zakarya, “Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction,” Neural networks, vol. 145, pp. 233–247, 2022.
  65. F. Zhou, L. Li, K. Zhang, and G. Trajcevski, “Urban flow prediction with spatial–temporal neural odes,” TRC, vol. 124, p. 102912, 2021.
  66. H. Peng, B. Du, M. Liu, M. Liu, S. Ji, S. Wang, X. Zhang, and L. He, “Dynamic graph convolutional network for long-term traffic flow prediction with reinforcement learning,” Information Sciences, vol. 578, pp. 401–416, 2021.
  67. S. Wang, H. Miao, J. Li, and J. Cao, “Spatio-temporal knowledge transfer for urban crowd flow prediction via deep attentive adaptation networks,” IEEE TITS, vol. 23, no. 5, pp. 4695–4705, 2021.
  68. S. Wang, M. Zhang, H. Miao, Z. Peng, and P. S. Yu, “Multivariate correlation-aware spatio-temporal graph convolutional networks for multi-scale traffic prediction,” ACM TIST, vol. 13, no. 3, pp. 1–22, 2022.
  69. Z. Zhou, Y. Wang, X. Xie, L. Chen, and C. Zhu, “Foresee urban sparse traffic accidents: A spatiotemporal multi-granularity perspective,” IEEE TKDE, vol. 34, no. 8, pp. 3786–3799, 2020.
  70. Z. Wang, R. Jiang, Z. Cai, Z. Fan, X. Liu, K.-S. Kim, X. Song, and R. Shibasaki, “Spatio-temporal-categorical graph neural networks for fine-grained multi-incident co-prediction,” in CIKM, 2021, pp. 2060–2069.
  71. X. Wu, C. Huang, C. Zhang, and N. V. Chawla, “Hierarchically structured transformer networks for fine-grained spatial event forecasting,” in WWW, 2020, pp. 2320–2330.
  72. Q. Wang, C. Xu, W. Zhang, and J. Li, “Graphtte: Travel time estimation based on attention-spatiotemporal graphs,” IEEE Signal Processing Letters, vol. 28, pp. 239–243, 2021.
  73. H. Wang, Z. Zhang, Z. Fan, J. Chen, L. Zhang, R. Shibasaki, and X. Song, “Multitask weakly supervised learning for origin destination travel time estimation,” arXiv preprint arXiv:2301.05336, 2023.
  74. G. Jin, H. Yan, F. Li, J. Huang, and Y. Li, “Spatio-temporal dual graph neural networks for travel time estimation,” arXiv preprint arXiv:2105.13591, 2021.
  75. G. Jin, H. Yan, F. Li, Y. Li, and J. Huang, “Hierarchical neural architecture search for travel time estimation,” in SIGSPATIAL, 2021, pp. 91–94.
  76. G. Jin, M. Wang, J. Zhang, H. Sha, and J. Huang, “Stgnn-tte: Travel time estimation via spatial–temporal graph neural network,” Future Generation Computer Systems, vol. 126, pp. 70–81, 2022.
  77. J. Jiang, D. Pan, H. Ren, X. Jiang, C. Li, and J. Wang, “Self-supervised trajectory representation learning with temporal regularities and travel semantics,” in ICDE.   IEEE, 2023, pp. 843–855.
  78. Y. Peng, G. Zhang, X. Li, and L. Zheng, “Stirnet: A spatial-temporal interaction-aware recursive network for human trajectory prediction,” in ICCV, 2021, pp. 2285–2293.
  79. L. Shi, L. Wang, C. Long, S. Zhou, M. Zhou, Z. Niu, and G. Hua, “Sgcn: Sparse graph convolution network for pedestrian trajectory prediction,” in CVPR, 2021, pp. 8994–9003.
  80. A. Mohamed, K. Qian, M. Elhoseiny, and C. Claudel, “Social-stgcnn: A social spatio-temporal graph convolutional neural network for human trajectory prediction,” in CVPR, 2020, pp. 14 424–14 432.
  81. S. Malla, C. Choi, and B. Dariush, “Social-stage: Spatio-temporal multi-modal future trajectory forecast,” in ICRA.   IEEE, 2021, pp. 13 938–13 944.
  82. Y. Liu, L. Yao, B. Li, X. Wang, and C. Sammut, “Social graph transformer networks for pedestrian trajectory prediction in complex social scenarios,” in CIKM, 2022, pp. 1339–1349.
  83. J. Lian, W. Ren, L. Li, Y. Zhou, and B. Zhou, “Ptp-stgcn: pedestrian trajectory prediction based on a spatio-temporal graph convolutional neural network,” Applied Intelligence, pp. 1–17, 2022.
  84. Y. Lin, N. Mago, Y. Gao, Y. Li, Y.-Y. Chiang, C. Shahabi, and J. L. Ambite, “Exploiting spatiotemporal patterns for accurate air quality forecasting using deep learning,” in SIGSPATIAL, 2018, pp. 359–368.
  85. J. Han, H. Liu, H. Zhu, H. Xiong, and D. Dou, “Joint air quality and weather prediction based on multi-adversarial spatiotemporal networks,” in AAAI, vol. 35, no. 5, 2021, pp. 4081–4089.
  86. H. Lin, Z. Gao, Y. Xu, L. Wu, L. Li, and S. Z. Li, “Conditional local convolution for spatio-temporal meteorological forecasting,” in AAAI, vol. 36, no. 7, 2022, pp. 7470–7478.
  87. T. Stańczyk and S. Mehrkanoon, “Deep graph convolutional networks for wind speed prediction,” arXiv preprint arXiv:2101.10041, 2021.
  88. N. Rathore, P. Rathore, A. Basak, S. H. Nistala, and V. Runkana, “Multi scale graph wavenet for wind speed forecasting,” in Big Data.   IEEE, 2021, pp. 4047–4053.
  89. L. Xia, C. Huang, Y. Xu, P. Dai, L. Bo, X. Zhang, and T. Chen, “Spatial-temporal sequential hypergraph network for crime prediction with dynamic multiplex relation learning.” in IJCAI, 2021, pp. 1631–1637.
  90. Z. Li, C. Huang, L. Xia, Y. Xu, and J. Pei, “Spatial-temporal hypergraph self-supervised learning for crime prediction,” in ICDE.   IEEE, 2022, pp. 2984–2996.
  91. M. Sun, P. Zhou, H. Tian, Y. Liao, and H. Xie, “Spatial-temporal attention network for crime prediction with adaptive graph learning,” in ICANN.   Springer, 2022, pp. 656–669.
  92. S. F. Tekin and S. S. Kozat, “Crime prediction with graph neural networks and multivariate normal distributions,” Signal, Image and Video Processing, pp. 1–7, 2022.
  93. Y. Zhang and T. Cheng, “Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events,” Computers, Environment and Urban Systems, vol. 79, p. 101403, 2020.
  94. G. Jin, C. Liu, Z. Xi, H. Sha, Y. Liu, and J. Huang, “Adaptive dual-view wavenet for urban spatial–temporal event prediction,” Information Sciences, vol. 588, pp. 315–330, 2022.
  95. X. Yang, F. Zhang, P. Sun, X. Li, Z. Du, and R. Liu, “A spatio-temporal graph-guided convolutional lstm for tropical cyclones precipitation nowcasting,” Applied Soft Computing, vol. 124, p. 109003, 2022.
  96. J. Zhou, J. Xiang, and S. Huang, “Classification and prediction of typhoon levels by satellite cloud pictures through gc–lstm deep learning model,” Sensors, vol. 20, no. 18, p. 5132, 2020.
  97. X. Zhang, W. Reichard-Flynn, M. Zhang, M. Hirn, and Y. Lin, “Spatiotemporal graph convolutional networks for earthquake source characterization,” Journal of Geophysical Research: Solid Earth, vol. 127, no. 11, p. e2022JB024401, 2022.
  98. B. Feng and G. Fox, “Gtrans: Spatiotemporal autoregressive transformer with graph embeddings for nowcasting extreme events,” arXiv preprint arXiv:2201.06717, 2022.
  99. L. Wang, A. Adiga, J. Chen, A. Sadilek, S. Venkatramanan, and M. Marathe, “Causalgnn: Causal-based graph neural networks for spatio-temporal epidemic forecasting,” in AAAI, vol. 36, no. 11, 2022, pp. 12 191–12 199.
  100. F. Xie, Z. Zhang, L. Li, B. Zhou, and Y. Tan, “Epignn: Exploring spatial transmission with graph neural network for regional epidemic forecasting,” arXiv preprint arXiv:2208.11517, 2022.
  101. Z. Li, X. Luo, B. Wang, A. L. Bertozzi, and J. Xin, “A study on graph-structured recurrent neural networks and sparsification with application to epidemic forecasting,” in Optimization of Complex Systems: Theory, Models, Algorithms and Applications.   Springer, 2020, pp. 730–739.
  102. S. Yu, F. Xia, S. Li, M. Hou, and Q. Z. Sheng, “Spatio-temporal graph learning for epidemic prediction,” ACM TIST, 2023.
  103. A. Kapoor, X. Ben, L. Liu, B. Perozzi, M. Barnes, M. Blais, and S. O’Banion, “Examining covid-19 forecasting using spatio-temporal graph neural networks,” arXiv preprint arXiv:2007.03113, 2020.
  104. V. La Gatta, V. Moscato, M. Postiglione, and G. Sperli, “An epidemiological neural network exploiting dynamic graph structured data applied to the covid-19 outbreak,” IEEE TBD, vol. 7, no. 1, pp. 45–55, 2020.
  105. S. Deng, S. Wang, H. Rangwala, L. Wang, and Y. Ning, “Graph message passing with cross-location attentions for long-term ili prediction,” arXiv preprint arXiv:1912.10202, 2019.
  106. Y. Lu, W. Wang, X. Hu, P. Xu, S. Zhou, and M. Cai, “Vehicle trajectory prediction in connected environments via heterogeneous context-aware graph convolutional networks,” IEEE TITS, 2022.
  107. X. Mo, Z. Huang, Y. Xing, and C. Lv, “Multi-agent trajectory prediction with heterogeneous edge-enhanced graph attention network,” TITS, vol. 23, no. 7, pp. 9554–9567, 2022.
  108. X. Mo, Y. Xing, H. Liu, and C. Lv, “Map-adaptive multimodal trajectory prediction using hierarchical graph neural networks,” IEEE Robotics and Automation Letters, 2023.
  109. Y. Liang, Y. Xia, S. Ke, Y. Wang, Q. Wen, J. Zhang, Y. Zheng, and R. Zimmermann, “Airformer: Predicting nationwide air quality in china with transformers,” arXiv preprint arXiv:2211.15979, 2022.
  110. H. Zhou, F. Zhang, Z. Du, and R. Liu, “Forecasting pm2. 5 using hybrid graph convolution-based model considering dynamic wind-field to offer the benefit of spatial interpretability,” Environmental Pollution, vol. 273, p. 116473, 2021.
  111. S. Wang, Y. Li, J. Zhang, Q. Meng, L. Meng, and F. Gao, “Pm2. 5-gnn: A domain knowledge enhanced graph neural network for pm2. 5 forecasting,” in SIGSPATIAL, 2020, pp. 163–166.
  112. Y. Liang, S. Ke, J. Zhang, X. Yi, and Y. Zheng, “Geoman: Multi-level attention networks for geo-sensory time series prediction.” in IJCAI, vol. 2018, 2018, pp. 3428–3434.
  113. S. Chen, J. A. Zwart, and X. Jia, “Physics-guided graph meta learning for predicting water temperature and streamflow in stream networks,” in SIGKDD, 2022, pp. 2752–2761.
  114. X. Jia, J. Zwart, J. Sadler, A. Appling, S. Oliver, S. Markstrom, J. Willard, S. Xu, M. Steinbach, J. Read, et al., “Physics-guided recurrent graph model for predicting flow and temperature in river networks,” in SDM.   SIAM, 2021, pp. 612–620.
  115. H. Lira, L. Martí, and N. Sanchez-Pi, “Frost forecasting model using graph neural networks with spatio-temporal attention,” in AI: Modeling Oceans and Climate Change Workshop at ICLR 2021, 2021.
  116. M. Khodayar and J. Wang, “Spatio-temporal graph deep neural network for short-term wind speed forecasting,” IEEE TSE, vol. 10, no. 2, pp. 670–681, 2018.
  117. Y. Qian, L. Pan, P. Wu, and Z. Xia, “Gest: A grid embedding based spatio-temporal correlation model for crime prediction,” in DSC.   IEEE, 2020, pp. 1–7.
  118. X. Song, H. Wang, and B. Zhou, “Dgcn-rs: A dilated graph convolutional networks jointly modelling relation and semantic for multi-event forecasting,” in ICONIP.   Springer, 2021, pp. 666–676.
  119. G. Jin, H. Sha, Y. Feng, Q. Cheng, and J. Huang, “Gsen: An ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction,” Neurocomputing, vol. 455, pp. 353–367, 2021.
  120. B. Wang, X. Luo, F. Zhang, B. Yuan, A. L. Bertozzi, and P. J. Brantingham, “Graph-based deep modeling and real time forecasting of sparse spatio-temporal data,” arXiv preprint arXiv:1804.00684, 2018.
  121. C. Wang, Z. Lin, X. Yang, J. Sun, M. Yue, and C. Shahabi, “Hagen: Homophily-aware graph convolutional recurrent network for crime forecasting,” in AAAI, vol. 36, no. 4, 2022, pp. 4193–4200.
  122. J. Feng, H. Sha, Y. Ding, L. Yan, and Z. Yu, “Graph convolution based spatial-temporal attention lstm model for flood forecasting,” in IJCNN.   IEEE, 2022, pp. 1–8.
  123. F. Yuan, Y. Xu, Q. Li, and A. Mostafavi, “Spatio-temporal graph convolutional networks for road network inundation status prediction during urban flooding,” Computers, Environment and Urban Systems, vol. 97, p. 101870, 2022.
  124. G. Jin, C. Zhu, X. Chen, H. Sha, X. Hu, and J. Huang, “Ufsp-net: a neural network with spatio-temporal information fusion for urban fire situation prediction,” in IOP Conference Series: Materials Science and Engineering, vol. 853, no. 1.   IOP Publishing, 2020, p. 012050.
  125. H. Farahmand, Y. Xu, and A. Mostafavi, “A spatial-temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features,” arXiv preprint arXiv:2111.08450, 2021.
  126. A. Doğan and E. Demir, “Structural recurrent neural network models for earthquake prediction,” Neural Computing and Applications, vol. 34, no. 13, pp. 11 049–11 062, 2022.
  127. I. W. McBrearty and G. C. Beroza, “Earthquake location and magnitude estimation with graph neural networks,” in ICIP.   IEEE, 2022, pp. 3858–3862.
  128. K. M. Ngoc and M. Lee, “Forecasting covid-19 confirmed cases in south korea using spatio-temporal graph neural networks.” International Journal of Contents, vol. 17, no. 3, 2021.
  129. G. Panagopoulos, G. Nikolentzos, and M. Vazirgiannis, “Transfer graph neural networks for pandemic forecasting,” in AAAI, vol. 35, no. 6, 2021, pp. 4838–4845.
  130. J. Gao, R. Sharma, C. Qian, L. M. Glass, J. Spaeder, J. Romberg, J. Sun, and C. Xiao, “Stan: spatio-temporal attention network for pandemic prediction using real-world evidence,” Journal of the American Medical Informatics Association, vol. 28, no. 4, pp. 733–743, 2021.
  131. S. Deng, S. Wang, H. Rangwala, L. Wang, and Y. Ning, “Cola-gnn: Cross-location attention based graph neural networks for long-term ili prediction,” in CIKM, 2020, pp. 245–254.
  132. C. Sun, V. K. Kumarasamy, Y. Liang, D. Wu, and Y. Wang, “Using a layered ensemble of physics-guided graph attention networks to predict covid-19 trends,” Applied Artificial Intelligence, vol. 36, no. 1, p. 2055989, 2022.
  133. Y. Zheng, Z. Li, J. Xin, and G. Zhou, “A spatial-temporal graph based hybrid infectious disease model with application to covid-19,” arXiv preprint arXiv:2010.09077, 2020.
  134. Z. Wang, T. Xia, R. Jiang, X. Liu, K.-S. Kim, X. Song, and R. Shibasaki, “Forecasting ambulance demand with profiled human mobility via heterogeneous multi-graph neural networks,” in ICDE.   IEEE, 2021, pp. 1751–1762.
  135. R. Jin, T. Xia, X. Liu, T. Murata, and K.-S. Kim, “Predicting emergency medical service demand with bipartite graph convolutional networks,” IEEE Access, vol. 9, pp. 9903–9915, 2021.
  136. T. Munasinghe and B. Behlendorf, “Using graph neural networks to investigate the relationship between the socioeconomic factors and emergency medical service (ems) median response time in new york city,” in Big Data.   IEEE, 2022, pp. 6781–6783.
  137. R. Yu, Y. Sun, D. He, J. Gao, Z. Liu, and M. Yu, “Spatio-temporal graph cross-correlation auto-encoding network for wind power prediction,” International Journal of Machine Learning and Cybernetics, pp. 1–13, 2022.
  138. Z. Li, L. Ye, Y. Zhao, M. Pei, P. Lu, Y. Li, and B. Dai, “A spatiotemporal directed graph convolution network for ultra-short-term wind power prediction,” IEEE TSE, vol. 14, no. 1, pp. 39–54, 2022.
  139. J. Simeunović, B. Schubnel, P.-J. Alet, and R. E. Carrillo, “Spatio-temporal graph neural networks for multi-site pv power forecasting,” IEEE TSE, vol. 13, no. 2, pp. 1210–1220, 2021.
  140. B. Hui, D. Yan, W.-S. Ku, and W. Wang, “Predicting economic growth by region embedding: A multigraph convolutional network approach,” in CIKM, 2020, pp. 555–564.
  141. F. Xu, Y. Li, and S. Xu, “Attentional multi-graph convolutional network for regional economy prediction with open migration data,” in SIGKDD, 2020, pp. 2225–2233.
  142. M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in NeurIPS, vol. 29, 2016.
  143. W. Hamilton, Z. Ying, and J. Leskovec, “Inductive representation learning on large graphs,” in NeurIPS, vol. 30, 2017.
  144. P. Veličković, G. Cucurull, A. Casanova, A. Romero, P. Lio, and Y. Bengio, “Graph attention networks,” arXiv preprint arXiv:1710.10903, 2017.
  145. J. Zhou, G. Cui, S. Hu, Z. Zhang, C. Yang, Z. Liu, L. Wang, C. Li, and M. Sun, “Graph neural networks: A review of methods and applications,” AI Open, vol. 1, pp. 57–81, 2020.
  146. D. K. Hammond, P. Vandergheynst, and R. Gribonval, “Wavelets on graphs via spectral graph theory,” Applied and Computational Harmonic Analysis, vol. 30, no. 2, pp. 129–150, 2011.
  147. M. Lukoševičius and H. Jaeger, “Reservoir computing approaches to recurrent neural network training,” Computer science review, vol. 3, no. 3, pp. 127–149, 2009.
  148. S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation, vol. 9, no. 8, pp. 1735–1780, 1997.
  149. R. Dey and F. M. Salem, “Gate-variants of gated recurrent unit (gru) neural networks,” in MWSCAS.   IEEE, 2017, pp. 1597–1600.
  150. Y. N. Dauphin, A. Fan, M. Auli, and D. Grangier, “Language modeling with gated convolutional networks,” in ICML.   PMLR, 2017, pp. 933–941.
  151. A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio,” arXiv preprint arXiv:1609.03499, 2016.
  152. F. Yu and V. Koltun, “Multi-scale context aggregation by dilated convolutions,” arXiv preprint arXiv:1511.07122, 2015.
  153. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” NeurIPS, vol. 30, 2017.
  154. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  155. Q. Ni and M. Zhang, “Stgmn: A gated multi-graph convolutional network framework for traffic flow prediction,” Applied Intelligence, vol. 52, no. 13, pp. 15 026–15 039, 2022.
  156. Y. He, L. Li, X. Zhu, and K. L. Tsui, “Multi-graph convolutional-recurrent neural network (mgc-rnn) for short-term forecasting of transit passenger flow,” IEEE TITS, vol. 23, no. 10, pp. 18 155–18 174, 2022.
  157. Z. Xu, Y. Kang, Y. Cao, and Z. Li, “Spatiotemporal graph convolution multifusion network for urban vehicle emission prediction,” IEEE TNNLS, vol. 32, no. 8, pp. 3342–3354, 2020.
  158. Z. Shao, Z. Zhang, W. Wei, F. Wang, Y. Xu, X. Cao, and C. S. Jensen, “Decoupled dynamic spatial-temporal graph neural network for traffic forecasting,” arXiv preprint arXiv:2206.09112, 2022.
  159. Z. Wu, S. Pan, G. Long, J. Jiang, X. Chang, and C. Zhang, “Connecting the dots: Multivariate time series forecasting with graph neural networks,” in SIGKDD, 2020, pp. 753–763.
  160. B. Lu, X. Gan, H. Jin, L. Fu, and H. Zhang, “Spatiotemporal adaptive gated graph convolution network for urban traffic flow forecasting,” in CIKM, 2020, pp. 1025–1034.
  161. W. Zhang, F. Zhu, Y. Lv, C. Tan, W. Liu, X. Zhang, and F.-Y. Wang, “Adapgl: An adaptive graph learning algorithm for traffic prediction based on spatiotemporal neural networks,” TRC, vol. 139, p. 103659, 2022.
  162. S. Lan, Y. Ma, W. Huang, W. Wang, H. Yang, and P. Li, “Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting,” in ICML.   PMLR, 2022, pp. 11 906–11 917.
  163. K. Guo, Y. Hu, Z. Qian, Y. Sun, J. Gao, and B. Yin, “Dynamic graph convolution network for traffic forecasting based on latent network of laplace matrix estimation,” IEEE TITS, vol. 23, no. 2, pp. 1009–1018, 2020.
  164. C. Shang, J. Chen, and J. Bi, “Discrete graph structure learning for forecasting multiple time series,” arXiv preprint arXiv:2101.06861, 2021.
  165. K. Guo, Y. Hu, Y. Sun, S. Qian, J. Gao, and B. Yin, “Hierarchical graph convolution network for traffic forecasting,” in AAAI, vol. 35, no. 1, 2021, pp. 151–159.
  166. L. Chen, J. Xu, B. Wu, Y. Qian, Z. Du, Y. Li, and Y. Zhang, “Group-aware graph neural network for nationwide city air quality forecasting,” arXiv preprint arXiv:2108.12238, 2021.
  167. Y. Tang, J. He, and Z. Zhao, “Hgarn: Hierarchical graph attention recurrent network for human mobility prediction,” arXiv preprint arXiv:2210.07765, 2022.
  168. S. Agarwal, R. Sawhney, M. Thakkar, P. Nakov, J. Han, and T. Derr, “Think: Temporal hypergraph hyperbolic network,” in ICDM.   IEEE, 2022, pp. 849–854.
  169. Y. Qin, Y. Fang, H. Luo, F. Zhao, and C. Wang, “Dmgcrn: Dynamic multi-graph convolution recurrent network for traffic forecasting,” arXiv preprint arXiv:2112.02264, 2021.
  170. S. Liu, X. Chen, Z. Wu, L. Deng, H. Su, and K. Zheng, “Hega: Heterogeneous graph aggregation network for trajectory prediction in high-density traffic,” in CIKM, 2022, pp. 1319–1328.
  171. Q. Zhou, J. Gu, X. Lu, F. Zhuang, Y. Zhao, Q. Wang, and X. Zhang, “Modeling heterogeneous relations across multiple modes for potential crowd flow prediction,” in AAAI, vol. 35, no. 5, 2021, pp. 4723–4731.
  172. S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in AAAI, vol. 33, no. 01, 2019, pp. 922–929.
  173. H. Wang, R. Zhang, X. Cheng, and L. Yang, “Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network,” IEEE TITS, vol. 23, no. 9, pp. 16 137–16 147, 2022.
  174. B. N. Oreshkin, A. Amini, L. Coyle, and M. Coates, “Fc-gaga: Fully connected gated graph architecture for spatio-temporal traffic forecasting,” in AAAI, vol. 35, no. 10, 2021, pp. 9233–9241.
  175. Y. Fang, Y. Qin, H. Luo, F. Zhao, B. Xu, C. Wang, and L. Zeng, “Spatio-temporal meets wavelet: Disentangled traffic flow forecasting via efficient spectral graph attention network,” arXiv e-prints, pp. arXiv–2112, 2021.
  176. D. Cao, Y. Wang, J. Duan, C. Zhang, X. Zhu, C. Huang, Y. Tong, B. Xu, J. Bai, J. Tong, et al., “Spectral temporal graph neural network for multivariate time-series forecasting,” in NeurIPS, vol. 33, 2020, pp. 17 766–17 778.
  177. S. Zheng, Z. Gao, W. Cao, J. Bian, and T.-Y. Liu, “Hierst: A unified hierarchical spatial-temporal framework for covid-19 trend forecasting,” in CIKM, 2021, pp. 4383–4392.
  178. B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio, “N-beats: Neural basis expansion analysis for interpretable time series forecasting,” arXiv preprint arXiv:1905.10437, 2019.
  179. T. Xia, J. Lin, Y. Li, J. Feng, P. Hui, F. Sun, D. Guo, and D. Jin, “3dgcn: 3-dimensional dynamic graph convolutional network for citywide crowd flow prediction,” ACM TKDD, vol. 15, no. 6, pp. 1–21, 2021.
  180. C. Zheng, X. Fan, S. Pan, Z. Wu, C. Wang, and P. S. Yu, “Spatio-temporal joint graph convolutional networks for traffic forecasting,” arXiv preprint arXiv:2111.13684, 2021.
  181. T. Wang, J. Chen, J. Lü, K. Liu, A. Zhu, H. Snoussi, and B. Zhang, “Synchronous spatiotemporal graph transformer: A new framework for traffic data prediction,” IEEE TNNLS, 2022.
  182. H. Li, D. Jin, X. Li, J. Huang, and J. Yoo, “Multi-task synchronous graph neural networks for traffic spatial-temporal prediction,” in SIGSPATIAL, 2021, pp. 137–140.
  183. Y. Fang, Y. Qin, H. Luo, F. Zhao, L. Zeng, B. Hui, and C. Wang, “Cdgnet: A cross-time dynamic graph-based deep learning model for traffic forecasting,” arXiv preprint arXiv:2112.02736, 2021.
  184. Y. Fang, F. Zhao, Y. Qin, H. Luo, and C. Wang, “Learning all dynamics: Traffic forecasting via locality-aware spatio-temporal joint transformer,” IEEE TITS, vol. 23, no. 12, pp. 23 433–23 446, 2022.
  185. Z. Pan, S. Ke, X. Yang, Y. Liang, Y. Yu, J. Zhang, and Y. Zheng, “Autostg: Neural architecture search for predictions of spatio-temporal graph,” in WWW, 2021, pp. 1846–1855.
  186. H. Liu, K. Simonyan, and Y. Yang, “Darts: Differentiable architecture search,” arXiv preprint arXiv:1806.09055, 2018.
  187. G. Jin, H. Yan, F. Li, Y. Li, and J. Huang, “Dual graph convolution architecture search for travel time estimation,” ACM TITS, vol. 14, no. 4, pp. 1–23, 2023.
  188. C. Wang, K. Zhang, H. Wang, and B. Chen, “Auto-stgcn: Autonomous spatial-temporal graph convolutional network search,” ACM TKDD, 2022.
  189. X. Wu, D. Zhang, C. Guo, C. He, B. Yang, and C. S. Jensen, “Autocts: Automated correlated time series forecasting,” VLDB, vol. 15, no. 4, pp. 971–983, 2021.
  190. G. Jin, H. Sha, Z. Xi, and J. Huang, “Urban hotspot forecasting via automated spatio-temporal information fusion,” Applied Soft Computing, p. 110087, 2023.
  191. S. Ke, Z. Pan, T. He, Y. Liang, J. Zhang, and Y. Zheng, “Autostg+: An automatic framework to discover the optimal network for spatio-temporal graph prediction,” Artificial Intelligence, p. 103899, 2023.
  192. A. Khaled, A. M. T. Elsir, and Y. Shen, “Tfgan: Traffic forecasting using generative adversarial network with multi-graph convolutional network,” Knowledge-Based Systems, vol. 249, p. 108990, 2022.
  193. Z. Huang, W. Zhang, D. Wang, and Y. Yin, “A gan framework-based dynamic multi-graph convolutional network for origin–destination-based ride-hailing demand prediction,” Information Sciences, vol. 601, pp. 129–146, 2022.
  194. J. Jin, D. Rong, T. Zhang, Q. Ji, H. Guo, Y. Lv, X. Ma, and F.-Y. Wang, “A gan-based short-term link traffic prediction approach for urban road networks under a parallel learning framework,” IEEE TITS, vol. 23, no. 9, pp. 16 185–16 196, 2022.
  195. C. Liu, Z. Xiao, D. Wang, M. Cheng, H. Chen, and J. Cai, “Foreseeing private car transfer between urban regions with multiple graph-based generative adversarial networks,” World Wide Web, vol. 25, no. 6, pp. 2515–2534, 2022.
  196. H. Miao, J. Shen, J. Cao, J. Xia, and S. Wang, “Mba-stnet: Bayes-enhanced discriminative multi-task learning for flow prediction,” IEEE TKDE, 2022.
  197. Z. Pan, Y. Liang, W. Wang, Y. Yu, Y. Zheng, and J. Zhang, “Urban traffic prediction from spatio-temporal data using deep meta learning,” in SIGKDD, 2019, pp. 1720–1730.
  198. Z. Pan, W. Zhang, Y. Liang, W. Zhang, Y. Yu, J. Zhang, and Y. Zheng, “Spatio-temporal meta learning for urban traffic prediction,” IEEE TKDE, vol. 34, no. 3, pp. 1462–1476, 2020.
  199. R. Jiang, Z. Wang, J. Yong, P. Jeph, Q. Chen, Y. Kobayashi, X. Song, S. Fukushima, and T. Suzumura, “Spatio-temporal meta-graph learning for traffic forecasting,” arXiv preprint arXiv:2211.14701, 2022.
  200. B. Lu, X. Gan, W. Zhang, H. Yao, L. Fu, and X. Wang, “Spatio-temporal graph few-shot learning with cross-city knowledge transfer,” in SIGKDD, 2022, pp. 1162–1172.
  201. J. Mo and Z. Gong, “Cross-city multi-granular adaptive transfer learning for traffic flow prediction,” IEEE TKDE, 2022.
  202. X. Liu, Y. Liang, C. Huang, Y. Zheng, B. Hooi, and R. Zimmermann, “When do contrastive learning signals help spatio-temporal graph forecasting?” in SIGSPATIAL, 2022, pp. 1–12.
  203. Y. You, T. Chen, Y. Sui, T. Chen, Z. Wang, and Y. Shen, “Graph contrastive learning with augmentations,” in NeurIPS, vol. 33, 2020, pp. 5812–5823.
  204. R. Li, T. Zhong, X. Jiang, G. Trajcevski, J. Wu, and F. Zhou, “Mining spatio-temporal relations via self-paced graph contrastive learning,” in SIGKDD, 2022, pp. 936–944.
  205. J. Ji, J. Wang, C. Huang, J. Wu, B. Xu, Z. Wu, J. Zhang, and Y. Zheng, “Spatio-temporal self-supervised learning for traffic flow prediction,” arXiv preprint arXiv:2212.04475, 2022.
  206. R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” in NeurIPS, vol. 31, 2018.
  207. Z. Fang, Q. Long, G. Song, and K. Xie, “Spatial-temporal graph ode networks for traffic flow forecasting,” in SIGKDD, 2021, pp. 364–373.
  208. M. Jin, Y. Zheng, Y.-F. Li, S. Chen, B. Yang, and S. Pan, “Multivariate time series forecasting with dynamic graph neural odes,” IEEE TKDE, 2022.
  209. Y. Liang, K. Ouyang, Y. Wang, Z. Pan, Y. Yin, H. Chen, J. Zhang, Y. Zheng, D. S. Rosenblum, and R. Zimmermann, “Mixed-order relation-aware recurrent neural networks for spatio-temporal forecasting,” IEEE TKDE, 2022.
  210. J. Choi, H. Choi, J. Hwang, and N. Park, “Graph neural controlled differential equations for traffic forecasting,” in AAAI, vol. 36, no. 6, 2022, pp. 6367–6374.
  211. G. E. Karniadakis, I. G. Kevrekidis, L. Lu, P. Perdikaris, S. Wang, and L. Yang, “Physics-informed machine learning,” Nature Reviews Physics, vol. 3, no. 6, pp. 422–440, 2021.
  212. T. Mallick, P. Balaprakash, E. Rask, and J. Macfarlane, “Transfer learning with graph neural networks for short-term highway traffic forecasting,” in ICPR.   IEEE, 2021, pp. 10 367–10 374.
  213. Y. Huang, X. Song, S. Zhang, and J. James, “Transfer learning in traffic prediction with graph neural networks,” in ITSC.   IEEE, 2021, pp. 3732–3737.
  214. B. An, A. Vahedian, X. Zhou, W. N. Street, and Y. Li, “Hintnet: Hierarchical knowledge transfer networks for traffic accident forecasting on heterogeneous spatio-temporal data,” in SDM.   SIAM, 2022, pp. 334–342.
  215. Y. Tang, A. Qu, A. H. Chow, W. H. Lam, S. Wong, and W. Ma, “Domain adversarial spatial-temporal network: A transferable framework for short-term traffic forecasting across cities,” in CIKM, 2022, pp. 1905–1915.
  216. Y. Liang, G. Huang, and Z. Zhao, “Cross-mode knowledge adaptation for bike sharing demand prediction using domain-adversarial graph neural networks,” arXiv preprint arXiv:2211.08903, 2022.
  217. P. Deng, Y. Zhao, J. Liu, X. Jia, and M. Wang, “Spatio-temporal neural structural causal models for bike flow prediction,” arXiv preprint arXiv:2301.07843, 2023.
  218. Y. Wang, A. Smola, D. Maddix, J. Gasthaus, D. Foster, and T. Januschowski, “Deep factors for forecasting,” in ICML.   PMLR, 2019, pp. 6607–6617.
  219. Y. Ovadia, E. Fertig, J. Ren, Z. Nado, D. Sculley, S. Nowozin, J. Dillon, B. Lakshminarayanan, and J. Snoek, “Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift,” in NeurIPS, vol. 32, 2019.
  220. X. Wang, H. Liu, C. Shi, and C. Yang, “Be confident! towards trustworthy graph neural networks via confidence calibration,” in NeurIPS, vol. 34, 2021, pp. 23 768–23 779.
  221. D. Wu, L. Gao, M. Chinazzi, X. Xiong, A. Vespignani, Y.-A. Ma, and R. Yu, “Quantifying uncertainty in deep spatiotemporal forecasting,” in SIGKDD, 2021, pp. 1841–1851.
  222. H. Wen, Y. Lin, Y. Xia, H. Wan, R. Zimmermann, and Y. Liang, “Diffstg: Probabilistic spatio-temporal graph forecasting with denoising diffusion models,” arXiv preprint arXiv:2301.13629, 2023.
  223. Z. Shao, Z. Zhang, F. Wang, and Y. Xu, “Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting,” in SIGKDD, 2022, pp. 1567–1577.
  224. K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick, “Masked autoencoders are scalable vision learners,” in CVPR, 2022, pp. 16 000–16 009.
  225. W. Zhang, Z. Lin, Y. Shen, Y. Li, Z. Yang, and B. Cui, “Deep and flexible graph neural architecture search,” in ICML.   PMLR, 2022, pp. 26 362–26 374.
  226. P. Xu, L. Zhang, X. Liu, J. Sun, Y. Zhao, H. Yang, and B. Yu, “Do not train it: A linear neural architecture search of graph neural networks,” in ICML.   PMLR, 2023, pp. 38 826–38 847.
  227. Y. Du, J. Wang, W. Feng, S. Pan, T. Qin, R. Xu, and C. Wang, “Adarnn: Adaptive learning and forecasting of time series,” in CIKM, 2021, pp. 402–411.
  228. Y. Xia, Y. Liang, H. Wen, X. Liu, K. Wang, Z. Zhou, and R. Zimmermann, “Deciphering spatio-temporal graph forecasting: A causal lens and treatment,” arXiv preprint arXiv:2309.13378, 2023.
  229. X. Liu, Y. Liang, C. Huang, H. Hu, Y. Cao, B. Hooi, and R. Zimmermann, “Do we really need graph neural networks for traffic forecasting?” arXiv preprint arXiv:2301.12603, 2023.
  230. A. Zeng, M. Chen, L. Zhang, and Q. Xu, “Are transformers effective for time series forecasting?” in AAAI, vol. 37, no. 9, 2023, pp. 11 121–11 128.
  231. S.-A. Chen, C.-L. Li, N. Yoder, S. O. Arik, and T. Pfister, “Tsmixer: An all-mlp architecture for time series forecasting,” arXiv preprint arXiv:2303.06053, 2023.
Citations (125)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now