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Spatiotemporal forecasting techniques are significant for various domains such as transportation, energy, and weather.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Vector autoregressions
James H Stock and Mark W Watson · 2001
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Utilizing real-world transportation data for accurate traffic prediction
Bei Pan, Ugur Demiryurek, and Cyrus Shahabi · 2012
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Wavenet: A generative model for raw audio
Aaron van den Oord, Sander Dieleman, Heiga Zen, Karen Simonyan, Oriol Vinyals, Alex Graves, Nal Kalchbrenner, Andrew Senior, and Koray Kavukcuoglu · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2018
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Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
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Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova · 2019
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Albert: A lite bert for self-supervised learning of language representations
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut · 2019
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Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov · 2019
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Graph wavenet for deep spatial-temporal graph modeling
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, and Chengqi Zhang · 2019
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Adaptive graph convolutional recurrent network for traffic forecasting
Lei Bai, Lina Yao, Can Li, Xianzhi Wang, and Can Wang · 2020
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Spectral temporal graph neural network for multivariate time-series forecasting
Defu Cao, Yujing Wang, Juanyong Duan, Ce Zhang, Xia Zhu, Congrui Huang, Yunhai Tong, Bixiong Xu, Jing Bai, Jie Tong, et al · 2020
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Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting
Chao Song, Youfang Lin, Shengnan Guo, and Huaiyu Wan · 2020
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Connecting the dots: Multivariate time series forecasting with graph neural networks
Zonghan Wu, Shirui Pan, Guodong Long, Jing Jiang, Xiaojun Chang, and Chengqi Zhang · 2020
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Spatial-temporal transformer networks for traffic flow forecasting
Mingxing Xu, Wenrui Dai, Chunmiao Liu, Xing Gao, Weiyao Lin, Guo-Jun Qi, and Hongkai Xiong · 2020
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei · 2021
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Z-gcnets: Time zigzags at graph convolutional networks for time series forecasting
Yuzhou Chen, Ignacio Segovia, and Yulia R Gel · 2021
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Towards spatio-temporal aware traffic time series forecasting
Razvan-Gabriel Cirstea, Bin Yang, Chenjuan Guo, Tung Kieu, and Shirui Pan · 2022
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A multi-view multi-task learning framework for multi-variate time series forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, and Ivor W Tsang · 2022
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2022
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Dstagnn: Dynamic spatial-temporal aware graph neural network for traffic flow forecasting
Shiyong Lan, Yitong Ma, Weikang Huang, Wenwu Wang, Hongyu Yang, and Pyang Li · 2022
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A time series is worth 64 words: Long-term forecasting with transformers
Yuqi Nie, Nam H Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam · 2022
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Enhancenet: Plugin neural networks for enhancing correlated time series forecasting
Razvan-Gabriel Cirstea, Tung Kieu, Chenjuan Guo, Bin Yang, and Sinno Jialin Pan · 2021
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St-norm: Spatial and temporal normalization for multi-variate time series forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, and Ivor W Tsang · 2021
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Spatial-temporal graph ode networks for traffic flow forecasting
Zheng Fang, Qingqing Long, Guojie Song, and Kunqing Xie · 2021
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Hierarchical graph convolution networks for traffic forecasting
Kan Guo, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin · 2021
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Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting
Shengnan Guo, Youfang Lin, Huaiyu Wan, Xiucheng Li, and Gao Cong · 2021
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Dynamic and multi-faceted spatio-temporal deep learning for traffic speed forecasting
Liangzhe Han, Bowen Du, Leilei Sun, Yanjie Fu, Yisheng Lv, and Hui Xiong · 2021
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Dl-traff: Survey and benchmark of deep learning models for urban traffic prediction
Renhe Jiang, Du Yin, Zhaonan Wang, Yizhuo Wang, Jiewen Deng, Hangchen Liu, Zekun Cai, Jinliang Deng, Xuan Song, and Ryosuke Shibasaki · 2021
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Zezhi Shao, Zhao Zhang, Fei Wang, Wei Wei, and Yongjun Xu · 2022
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Pre-training enhanced spatial-temporal graph neural network for multivariate time series forecasting
Zezhi Shao, Zhao Zhang, Fei Wang, and Yongjun Xu · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Pdformer: Propagation delay-aware dynamic long-range transformer for traffic flow prediction
Jiawei Jiang, Chengkai Han, Wayne Xin Zhao, and Jingyuan Wang · 2023
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Spatio-temporal meta-graph learning for traffic forecasting
Renhe Jiang, Zhaonan Wang, Jiawei Yong, Puneet Jeph, Quanjun Chen, Yasumasa Kobayashi, Xuan Song, Shintaro Fukushima, and Toyotaro Suzumura · 2023
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Ti-mae: Self-supervised masked time series autoencoders
Zhe Li, Zhongwen Rao, Lujia Pan, Pengyun Wang, and Zenglin Xu · 2023
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Spatio-temporal adaptive embedding makes vanilla transformer sota for traffic forecasting
Hangchen Liu, Zheng Dong, Renhe Jiang, Jiewen Deng, Jinliang Deng, Quanjun Chen, and Xuan Song · 2023
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Zezhi Shao, Fei Wang, Yongjun Xu, Wei Wei, Chengqing Yu, Zhao Zhang, Di Yao, Guangyin Jin, Xin Cao, Gao Cong, et al · 2023
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Disentangling structured components: Towards adaptive, interpretable and scalable time series forecasting
Jinliang Deng, Xiusi Chen, Renhe Jiang, Du Yin, Yi Yang, Xuan Song, and Ivor W Tsang · 2024
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