DNN-Based Prediction Model for Spatial-Temporal Data
文献类型：会议论文
| 作者 | Junbo Zhang; Yu Zheng; Dekang Qi; Ruiyuan Li; Xiuwen Yi |
| 出版日期 | 2016 |
| 会议名称 | ACM SIGSPATIAL 2016 |
| 会议地点 | California, USA |
| 英文摘要 | Advances in location-acquisition and wireless communication technologies have led to wider availability of spatio-temporal (ST) data, which has unique spatial properties (i.e. geographical hierarchy and distance) and temporal properties (i.e. closeness, period and trend). In this paper, we propose a Deep-learning-based prediction model for Spatio-Temporal data (DeepST). We leverage ST domain knowledge to design the architecture of DeepST, which is comprised of two components: spatio-temporal and global. The spatio-temporal component employs the framework of convolutional neural networks to simultaneously model spatial near and distant dependencies, and temporal closeness, period and trend. The global component is used to capture global factors, such as day of the week, weekday or week-end. Using DeepST, we build a real-time crowd flow fore-casting system called UrbanFlow1. Experiment results on diverse ST datasets verify DeepST’s ability to capture ST data’s spatio-temporal properties, showing the advantages of DeepST beyond four baseline methods. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10351]  |
| 专题 | 深圳先进技术研究院_数字所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Junbo Zhang,Yu Zheng,Dekang Qi,et al. DNN-Based Prediction Model for Spatial-Temporal Data[C]. 见:ACM SIGSPATIAL 2016. California, USA. |
入库方式： OAI收割
来源：深圳先进技术研究院
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