Contextualizing MLP-Mixers Spatiotemporally for Urban Data Forecast at Scale

Z. Wu, S. Pan, G. Long, J. Jiang, and C. Zhang, “Graph wavenet for deep spatial-temporal graph modeling,” in Proceedings of the 28th International Joint Conference on Artificial Intelligence , 2019, pp. 1907–1913

1913

Earlier work this paper cites.

C. Chen, K. Petty, A. Skabardonis, P. Varaiya, and Z. Jia, “Freeway performance measurement system: mining loop detector data,” Transportation Research Record , vol. 1748, no. 1, pp. 96–102, 2001

2001

Earlier work this paper cites.

S. Brakatsoulas, D. Pfoser, R. Salas, and C. Wenk, “On map-matching vehicle tracking data,” in Proceedings of the 31st international conference on Very large data bases , 2005, pp. 853–864

2005

Earlier work this paper cites.

M. Hummon, E. Ibanez, G. Brinkman, and D. Lew, “Sub-hour solar data for power system modeling from static spatial variability analysis,” National Renewable Energy Lab.(NREL), Golden, CO (United States), Tech. Rep., 2012

2012

Earlier work this paper cites.

Y. Zheng, X. Yi, M. Li, R. Li, Z. Shan, E. Chang, and T. Li, “Forecasting fine-grained air quality based on big data,” in Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining , 2015, pp. 2267–2276

2015

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008

2017

Earlier work this paper cites.

B. Yu, H. Yin, and Z. Zhu, “Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting,” in Proceedings of the 27th International Joint Conference on Artificial Intelligence , 2018, pp. 3634–3640

2018

Earlier work this paper cites.

Y. Li, R. Yu, C. Shahabi, and Y. Liu, “Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,” in International Conference on Learning Representations , 2018

2018

Earlier work this paper cites.

G. Lai, W.-C. Chang, Y. Yang, and H. Liu, “Modeling long-and short-term temporal patterns with deep neural networks,” in The 41st international ACM SIGIR conference on research & development in information retrieval , 2018, pp. 95–104

2018

Earlier work this paper cites.

S. Guo, Y. Lin, N. Feng, C. Song, and H. Wan, “Attention based spatial-temporal graph convolutional networks for traffic flow forecasting,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 922–929

2019

Earlier work this paper cites.

B. Srinivasan and B. Ribeiro, “On the equivalence between positional node embeddings and structural graph representations,” arXiv preprint arXiv:1910.00452 , 2019

Original

2019

Earlier work this paper cites.

Y.-H. H. Tsai, S. Bai, M. Yamada, L.-P. Morency, and R. Salakhutdinov, “Transformer dissection: a unified understanding of transformer’s attention via the lens of kernel,” arXiv preprint arXiv:1908.11775 , 2019

Original

2019

Earlier work this paper cites.

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga et al. , “Pytorch: An imperative style, high-performance deep learning library,” Advances in neural information processing systems , vol. 32, pp. 8026–8037, 2019

2019

Earlier work this paper cites.

L. Bai, L. Yao, C. Li, X. Wang, and C. Wang, “Adaptive graph convolutional recurrent network for traffic forecasting,” Advances in neural information processing systems , vol. 33, pp. 17 804–17 815, 2020

2020

Earlier work this paper cites.

C. Zheng, X. Fan, C. Wang, and J. Qi, “Gman: A graph multi-attention network for traffic prediction,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, 2020, pp. 1234–1241

2020

Earlier work this paper cites.

Q. Zhang, J. Chang, G. Meng, S. Xiang, and C. Pan, “Spatio-temporal graph structure learning for traffic forecasting,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 01, 2020, pp. 1177–1185

2020

Earlier work this paper cites.

M. Xu, W. Dai, C. Liu, X. Gao, W. Lin, G.-J. Qi, and H. Xiong, “Spatial-temporal transformer networks for traffic flow forecasting,” arXiv preprint arXiv:2001.02908 , 2020

Original

2020

Earlier work this paper cites.

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 Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York, NY, USA: Association for Computing Machinery, 2020, p. 753–763

2020

Earlier work this paper cites.

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,” Advances in neural information processing systems , vol. 33, pp. 17 766–17 778, 2020

2020

Earlier work this paper cites.

A. Pareja, G. Domeniconi, J. Chen, T. Ma, T. Suzumura, H. Kanezashi, T. Kaler, T. Schardl, and C. Leiserson, “Evolvegcn: Evolving graph convolutional networks for dynamic graphs,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 04, 2020, pp. 5363–5370

2020

Earlier work this paper cites.

F. Rodrigues and F. C. Pereira, “Beyond expectation: Deep joint mean and quantile regression for spatiotemporal problems,” IEEE transactions on neural networks and learning systems , vol. 31, no. 12, pp. 5377–5389, 2020

2020

Earlier work this paper cites.

V. P. Dwivedi, C. K. Joshi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson, “Benchmarking graph neural networks,” arXiv preprint arXiv:2003.00982 , 2020

Original

2020

Earlier work this paper cites.

H. Wu, J. Xu, J. Wang, and M. Long, “Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,” Advances in Neural Information Processing Systems , vol. 34, pp. 22 419–22 430, 2021

2021

Earlier work this paper cites.

S. Elsayed, D. Thyssens, A. Rashed, H. S. Jomaa, and L. Schmidt-Thieme, “Do we really need deep learning models for time series forecasting?” arXiv preprint arXiv:2101.02118 , 2021

Original

2021

Earlier work this paper cites.

I. O. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit et al. , “Mlp-mixer: An all-mlp architecture for vision,” Advances in neural information processing systems , vol. 34, pp. 24 261–24 272, 2021

2021

Earlier work this paper cites.

B. N. Oreshkin, A. Amini, L. Coyle, and M. Coates, “Fc-gaga: Fully connected gated graph architecture for spatio-temporal traffic forecasting,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 10, 2021, pp. 9233–9241

2021

Cited alongside, same era.

S. Guo, Y. Lin, H. Wan, X. Li, and G. Cong, “Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,” IEEE Transactions on Knowledge and Data Engineering , vol. 34, no. 11, pp. 5415–5428, 2021

2021

Cited alongside, same era.

H. Zhou, S. Zhang, J. Peng, S. Zhang, J. Li, H. Xiong, and W. Zhang, “Informer: Beyond efficient transformer for long sequence time-series forecasting,” in Proceedings of the AAAI conference on artificial intelligence , vol. 35, no. 12, 2021, pp. 11 106–11 115

2021

Cited alongside, same era.

T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, and R. Jin, “Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting,” in International Conference on Machine Learning . PMLR, 2022, pp. 27 268–27 286

X. Liu, Y. Xia, Y. Liang, J. Hu, Y. Wang, L. Bai, C. Huang, Z. Liu, B. Hooi, and R. Zimmermann, “Largest: A benchmark dataset for large-scale traffic forecasting,” arXiv preprint arXiv:2306.08259 , 2023

Original

2023

Closest in time.

M. Jin, H. Y. Koh, Q. Wen, D. Zambon, C. Alippi, G. I. Webb, I. King, and S. Pan, “A survey on graph neural networks for time series: Forecasting, classification, imputation, and anomaly detection,” arXiv preprint arXiv:2307.03759 , 2023

Original

2023

Closest in time.

A. Cini, I. Marisca, D. Zambon, and C. Alippi, “Graph deep learning for time series forecasting,” arXiv preprint arXiv:2310.15978 , 2023

Original

2023

Closest in time.

X. He, B. Hooi, T. Laurent, A. Perold, Y. LeCun, and X. Bresson, “A generalization of vit/mlp-mixer to graphs,” in International Conference on Machine Learning . PMLR, 2023, pp. 12 724–12 745

2023

Closest in time.