Smart City Gnosys

Smart city article details

Title	Decoupling Long- And Short-Term Patterns In Spatiotemporal Inference
ID_Doc	17754
Authors	Hu J.; Liang Y.; Fan Z.; Liu L.; Yin Y.; Zimmermann R.
Year	2024
Published	IEEE Transactions on Neural Networks and Learning Systems, 35, 11
DOI	http://dx.doi.org/10.1109/TNNLS.2023.3293814
Abstract	Sensors are the key to environmental monitoring, which impart benefits to smart cities in many aspects, such as providing real-time air quality information to assist human decision-making. However, it is impractical to deploy massive sensors due to the expensive costs, resulting in sparse data collection. Therefore, how to get fine-grained data measurement has long been a pressing issue. In this article, we aim to infer values at nonsensor locations based on observations from available sensors (termed spatiotemporal inference), where capturing spatiotemporal relationships among the data plays a critical role. Our investigations reveal two significant insights that have not been explored by previous works. First, data exhibit distinct patterns at both long- and short-term temporal scales, which should be analyzed separately. Second, short-term patterns contain more delicate relations, including those across spatial and temporal dimensions simultaneously, while long-term patterns involve high-level temporal trends. Based on these observations, we propose to decouple the modeling of short- and long-term patterns. Specifically, we introduce a joint spatiotemporal graph attention network to learn the relations across space and time for short-term patterns. Furthermore, we propose a graph recurrent network with a time skip strategy to alleviate the gradient vanishing problem and model the long-term dependencies. Experimental results on four public real-world datasets demonstrate that our method effectively captures both long- and short-term relations, achieving state-of-the-art performance against existing methods. © 2023 IEEE.
Author Keywords	Attention mechanism; graph neural network; spatiotemporal inference; urban computing

Similar Articles

Id	Similarity	Authors	Title	Published
23809	0.891	Ouyang X.; Li Y.; Guo D.; Huang W.; Yang X.; Yang Y.; Zhang J.; Li T.	Enhancing Few-Sample Spatio-Temporal Prediction Via Relational Fusion-Based Hypergraph Neural Network	Information Fusion, 121 (2025)
53042	0.891	Jin Z.; Xu J.; Huang R.; Shao W.; Xiao X.	Stm2Cn: A Multi-Graph Attention-Based Framework For Sensor Data Prediction In Smart Cities	Proceedings of the International Joint Conference on Neural Networks, 2022-July (2022)
46173	0.888	Ma Q.; Zhang Z.; Zhao X.; Li H.; Zhao H.; Wang Y.; Liu Z.; Wang W.	Rethinking Sensors Modeling: Hierarchical Information Enhanced Traffic Forecasting	International Conference on Information and Knowledge Management, Proceedings (2023)
60280	0.882	Li Z.; Xia L.; Tang J.; Xu Y.; Shi L.; Xia L.; Yin D.; Huang C.	Urbangpt: Spatio-Temporal Large Language Models	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2024)
58207	0.881	Zhou Z.; Yang K.; Sun W.; Wang B.; Zhou M.; Zong Y.; Wang Y.	Towards Learning In Grey Spatiotemporal Systems: A Prophet To Non-Consecutive Spatiotemporal Dynamics	2023 SIAM International Conference on Data Mining, SDM 2023 (2023)
52516	0.876	Dao M.-S.; Zetsu K.; Hoang D.-T.	Spatial-Temporal Graph Transformer Network For Spatial-Temporal Forecasting	Proceedings - 2024 IEEE International Conference on Big Data, BigData 2024 (2024)
37180	0.873	Liang Y.; Ouyang K.; Wang Y.; Pan Z.; Yin Y.; Chen H.; Zhang J.; Zheng Y.; Rosenblum D.S.; Zimmermann R.	Mixed-Order Relation-Aware Recurrent Neural Networks For Spatio-Temporal Forecasting	IEEE Transactions on Knowledge and Data Engineering, 35, 9 (2023)
52567	0.872	Jin G.; Liang Y.; Fang Y.; Shao Z.; Huang J.; Zhang J.; Zheng Y.	Spatio-Temporal Graph Neural Networks For Predictive Learning In Urban Computing: A Survey	IEEE Transactions on Knowledge and Data Engineering, 36, 10 (2024)
59603	0.871	Li Y.; Lin Y.; Gao Y.; Khan L.	Unified Spatio-Temporal Graph Neural Networks: Data-Driven Modeling For Social Science	Proceedings of the International Joint Conference on Neural Networks, 2022-July (2022)
52623	0.871	Guo X.; Huang F.; Yang D.; Tu C.; Yu Z.; Guo W.	Spatiotemporal Fracture Data Inference In Sparse Mobile Crowdsensing: A Graph- And Attention-Based Approach	IEEE/ACM Transactions on Networking, 32, 2 (2024)