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Spatio-temporal forecasting is essential for understanding future dynamics within real-world systems by leveraging historical data from multiple locations.
Long short-term memory
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Short-term traffic flow forecasting: An experimental comparison of time-series analysis and supervised learning
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Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Deep architecture for traffic flow prediction: deep belief networks with multitask learning
Wenhao Huang, Guojie Song, Haikun Hong, and Kunqing Xie · 2014
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Traffic flow prediction with big data: A deep learning approach
Yisheng Lv, Yanjie Duan, Wenwen Kang, Zhengxi Li, and Fei-Yue Wang · 2014
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Thomas N Kipf and Max Welling · 2016
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Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2017
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Attention is all you need
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The sensable city: A survey on the deployment and management for smart city monitoring
Rong Du, Paolo Santi, Ming Xiao, Athanasios V Vasilakos, and Carlo Fischione · 2018
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Algorithmic regularization in over-parameterized matrix sensing and neural networks with quadratic activations
Yuanzhi Li, Tengyu Ma, and Hongyang Zhang · 2018
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Geoman: Multi-level attention networks for geo-sensory time series prediction
Yuxuan Liang, Songyu Ke, Junbo Zhang, Xiuwen Yi, and Yu Zheng · 2018
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Deep multi-view spatial-temporal network for taxi demand prediction
Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, and Zhenhui Li · 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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Combining weather condition data to predict traffic flow: a gru-based deep learning approach
Da Zhang and Mansur R Kabuka · 2018
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Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction
Zheyi Pan, Zhaoyuan Wang, Weifeng Wang, Yong Yu, Junbo Zhang, and Yu Zheng · 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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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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Decoupled dynamic spatial-temporal graph neural network for traffic forecasting
Zezhi Shao, Zhao Zhang, Wei Wei, Fei Wang, Yongjun Xu, Xin Cao, and Christian S Jensen · 2022
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Mojtaba Valipour, Mehdi Rezagholizadeh, Ivan Kobyzev, and Ali Ghodsi · 2022
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Hierarchical traffic flow prediction based on spatial-temporal graph convolutional network
Hanqiu Wang, Rongqing Zhang, Xiang Cheng, and Liuqing Yang · 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Tian Zhou, Ziqing Ma, Qingsong Wen, Xue Wang, Liang Sun, and Rong Jin · 2022
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Yong Yu, Xiaosheng Si, Changhua Hu, and Jianxun 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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Deep learning for spatio-temporal data mining: A survey
Senzhang Wang, Jiannong Cao, and S Yu Philip · 2020
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Traffic flow prediction via spatial temporal graph neural network
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu · 2020
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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Spatio-temporal graph structure learning for traffic forecasting
Qi Zhang, Jianlong Chang, Gaofeng Meng, Shiming Xiang, and Chunhong Pan · 2020
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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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Spatial-temporal-decoupled masked pre-training for spatiotemporal forecasting
Haotian Gao, Renhe Jiang, Zheng Dong, Jinliang Deng, Yuxin Ma, and Xuan Song · 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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Spatio-temporal graph neural networks for predictive learning in urban computing: A survey
Guangyin Jin, Yuxuan Liang, Yuchen Fang, Zezhi Shao, Jincai Huang, Junbo Zhang, and Yu Zheng · 2023
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Airformer: Predicting nationwide air quality in china with transformers
Yuxuan Liang, Yutong Xia, Songyu Ke, Yiwei Wang, Qingsong Wen, Junbo Zhang, Yu Zheng, and Roger Zimmermann · 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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Mtlora: Low-rank adaptation approach for efficient multi-task learning
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Heterogeneity-informed meta-parameter learning for spatiotemporal time series forecasting
Zheng Dong, Renhe Jiang, Haotian Gao, Hangchen Liu, Jinliang Deng, Qingsong Wen, and Xuan Song · 2024
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Lora+: Efficient low rank adaptation of large models
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Position: What can large language models tell us about time series analysis
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Largest: A benchmark dataset for large-scale traffic forecasting
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Spatio-temporal fusion graph convolutional network for traffic flow forecasting
Ying Ma, Haijie Lou, Ming Yan, Fanghui Sun, and Guoqi Li · 2024
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Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web
Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, and Yuxuan Liang · 2024
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One fits all: Power general time series analysis by pretrained lm
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