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Traffic forecasting as a canonical task of multivariate time series forecasting has been a significant research topic in AI community.
Autoregressive conditional heteroskedasticity and changes in regime
Hamilton, J. D.; and Susmel, R. 1994 · 1994
Earlier work this paper cites.
Long short-term memory
Hochreiter, S.; and Schmidhuber, J. 1997 · 1997
Earlier work this paper cites.
Vector autoregressions
Stock, J. H.; and Watson, M. W. 2001 · 2001
Earlier work this paper cites.
Spatial-temporal transformer networks for traffic flow forecasting
Xu, M.; Dai, W.; Liu, C.; Gao, X.; Lin, W.; Qi, G.-J.; and Xiong, H. 2020 · 2001
Earlier work this paper cites.
Utilizing real-world transportation data for accurate traffic prediction
Pan, B.; Demiryurek, U.; and Shahabi, C. 2012 · 2012
Earlier work this paper cites.
Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J.; Gulcehre, C.; Cho, K.; and Bengio, Y. 2014 · 2014
Earlier work this paper cites.
Deep architecture for traffic flow prediction: Deep belief networks with multitask learning
Huang, W.; Song, G.; Hong, H.; and Xie, K. 2014 · 2014
Earlier work this paper cites.
Traffic flow prediction with big data: a deep learning approach
Lv, Y.; Duan, Y.; Kang, W.; Li, Z.; and Wang, F.-Y. 2014 · 2014
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M.; Bresson, X.; and Vandergheynst, P. 2016 · 2016
Earlier work this paper cites.
Ha, D.; Dai, A.; and Le, Q. V. 2016 · 2016
Earlier work this paper cites.
Wavenet: A generative model for raw audio
Oord, A. v. d.; Dieleman, S.; Zen, H.; Simonyan, K.; Vinyals, O.; Graves, A.; Kalchbrenner, N.; Senior, A.; and Kavukcuoglu, K. 2016 · 2016
Earlier work this paper cites.
Meta-learning with memory-augmented neural networks
Santoro, A.; Bartunov, S.; Botvinick, M.; Wierstra, D.; and Lillicrap, T. 2016 · 2016
Earlier work this paper cites.
Matching networks for one shot learning
Vinyals, O.; Blundell, C.; Lillicrap, T.; Wierstra, D.; et al. 2016 · 2016
Earlier work this paper cites.
Multi-Scale Context Aggregation by Dilated Convolutions
Yu, F.; and Koltun, V. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks
Kipf, T. N.; and Welling, M. 2017 · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
Earlier work this paper cites.
Veličković, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; and Bengio, Y. 2017 · 2017
Earlier work this paper cites.
Meta-graph based recommendation fusion over heterogeneous information networks
Zhao, H.; Yao, Q.; Li, J.; Song, Y.; and Lee, D. L. 2017 · 2017
Earlier work this paper cites.
Neural relational inference for interacting systems
Kipf, T.; Fetaya, E.; Wang, K.-C.; Welling, M.; and Zemel, R. 2018 · 2018
Earlier work this paper cites.
Modeling long-and short-term temporal patterns with deep neural networks
Lai, G.; Chang, W.-C.; Yang, Y.; and Liu, H. 2018 · 2018
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Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting
Li, Y.; Yu, R.; Shahabi, C.; and Liu, Y. 2018 · 2018
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Lc-rnn: A deep learning model for traffic speed prediction
Lv, Z.; Xu, J.; Zheng, K.; Yin, H.; Zhao, P.; and Zhou, X. 2018 · 2018
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Structured sequence modeling with graph convolutional recurrent networks
Seo, Y.; Defferrard, M.; Vandergheynst, P.; and Bresson, X. 2018 · 2018
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Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
Yu, B.; Yin, H.; and Zhu, Z. 2018 · 2018
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Dynamic spatial-temporal graph convolutional neural networks for traffic forecasting
Traffic flow prediction via spatial temporal graph neural network
Wang, X.; Ma, Y.; Wang, Y.; Jin, W.; Wang, X.; Tang, J.; Jia, C.; and Yu, J. 2020 · 2020
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Connecting the dots: Multivariate time series forecasting with graph neural networks
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; Chang, X.; and Zhang, C. 2020 · 2020
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Spatio-temporal graph structure learning for traffic forecasting
Zhang, Q.; Chang, J.; Meng, G.; Xiang, S.; and Pan, C. 2020 · 2020
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Gman: A graph multi-attention network for traffic prediction
Zheng, C.; Fan, X.; Wang, C.; and Qi, J. 2020 · 2020
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ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting
Deng, J.; Chen, X.; Jiang, R.; Song, X.; and Tsang, I. W. 2021 · 2021
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Diffmg: Differentiable meta graph search for heterogeneous graph neural networks
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Diao, Z.; Wang, X.; Zhang, D.; Liu, Y.; Xie, K.; and He, S. 2019 · 2019
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Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting
Geng, X.; Li, Y.; Wang, L.; Zhang, L.; Yang, Q.; Ye, J.; and Liu, Y. 2019 · 2019
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Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Gong, D.; Liu, L.; Le, V.; Saha, B.; Mansour, M. R.; Venkatesh, S.; and Hengel, A. v. d. 2019 · 2019
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Guo, S.; Lin, Y.; Feng, N.; Song, C.; and Wan, H. 2019 · 2019
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Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
Li, S.; Jin, X.; Xuan, Y.; Zhou, X.; Chen, W.; Wang, Y.-X.; and Yan, X. 2019 · 2019
Cited alongside, same era.
Temporal pattern attention for multivariate time series forecasting
Shih, S.-Y.; Sun, F.-K.; and Lee, H.-y. 2019 · 2019
Cited alongside, same era.
Graph WaveNet for Deep Spatial-Temporal Graph Modeling
Wu, Z.; Pan, S.; Long, G.; Jiang, J.; and Zhang, C. 2019 · 2019
Cited alongside, same era.
Ding, Y.; Yao, Q.; Zhao, H.; and Zhang, T. 2021 · 2021
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DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction
Jiang, R.; Yin, D.; Wang, Z.; Wang, Y.; Deng, J.; Liu, H.; Cai, Z.; Deng, J.; Song, X.; and Shibasaki, R. 2021 · 2021
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Graph neural network for traffic forecasting: A survey
Jiang, W.; and Luo, J. 2021 · 2021
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Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution
Li, F.; Feng, J.; Yan, H.; Jin, G.; Jin, D.; and Li, Y. 2021 · 2021
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Discrete Graph Structure Learning for Forecasting Multiple Time Series
Shang, C.; Chen, J.; and Bi, J. 2021 · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Xu, J.; Wang, J.; Long, M.; et al. 2021 · 2021
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Coupled Layer-wise Graph Convolution for Transportation Demand Prediction
Ye, J.; Sun, L.; Du, B.; Fu, Y.; and Xiong, H. 2021 · 2021
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Traffic Flow Forecasting with Spatial-Temporal Graph Diffusion Network
Zhang, X.; Huang, C.; Xu, Y.; Xia, L.; Dai, P.; Bo, L.; Zhang, J.; and Zheng, Y. 2021 · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H.; Zhang, S.; Peng, J.; Zhang, S.; Li, J.; Xiong, H.; and Zhang, W. 2021 · 2021
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Deep graph structure learning for robust representations: A survey
Zhu, Y.; Xu, W.; Zhang, J.; Liu, Q.; Wu, S.; and Wang, L. 2021 · 2021
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Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting
Lee, H.; Jin, S.; Chu, H.; Lim, H.; and Ko, S. 2022 · 2022
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Pre-training Enhanced Spatial-temporal Graph Neural Network for Multivariate Time Series Forecasting
Shao, Z.; Zhang, Z.; Wang, F.; and Xu, Y. 2022 · 2022
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Event-aware multimodal mobility nowcasting
Wang, Z.; Jiang, R.; Xue, H.; Salim, F. D.; Song, X.; and Shibasaki, R. 2022 · 2022
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