Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model
文献类型：期刊论文
| 作者 | Wang, Peixiao3; Zhang, Hengcai3; Lu, Feng2,3 |
| 刊名 | SPATIAL DATA AND INTELLIGENCE, SPATIALDI 2024
 |
| 出版日期 | 2024-10-01 |
| 卷号 | 14619页码:242-251 |
| 关键词 | Spatiotemporal prediction spatiotemporal data missing causal dilatation convolution graph attention network |
| DOI | 10.1007/978-981-97-2966-1_18 |
| 产权排序 | 1 |
| 文献子类 | Proceedings Paper |
| 英文摘要 | Spatiotemporal prediction is a research topic in urban planning and management. Most existing spatiotemporal prediction models currently face challenges. More specifically, most prediction models are sensitive to missing data, meaning most prediction models are only tested on spatiotemporal data assuming no missing data. Although missing data can be imputed, spatiotemporal prediction models with the capability of handling missing data are needed. In this study, we propose a novel missing-data-tolerant causal graph attention model called CGATM to address the above challenges. To enable the CGATM model to be tested on spatiotemporal data with missing data, we propose a novel missing data handling mechanism that automatically handles missing data according to the probability of data missing patterns. To improve the nonlinear fitting ability of the CGATMmodel, we propose a novel causal graph attention method that represents geospatial heterogeneity by adjacent nodes with different weights. In addition, we design the CGTAM model as an Imputer-Predictor architecture and define a novel loss function to optimize model parameters. The proposed model was validated on three real-world spatiotemporal datasets (traffic dataset, PM2.5 dataset, and temperature dataset). Experimental results showed that the proposed model has better prediction performance under four missing scenarios, and outperforms eight existing baselines regarding prediction accuracy. |
| WOS研究方向 | Computer Science ; Remote Sensing |
| WOS记录号 | WOS:001278296000018 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/207901]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Zhang, Hengcai |
| 作者单位 | 1.Fujian Collaborat Innovat Ctr Big Data Applicat G, Fuzhou 350003, Peoples R China 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Peixiao,Zhang, Hengcai,Lu, Feng. Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model[J]. SPATIAL DATA AND INTELLIGENCE, SPATIALDI 2024,2024,14619:242-251. |
| APA | Wang, Peixiao,Zhang, Hengcai,&Lu, Feng.(2024).Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model.SPATIAL DATA AND INTELLIGENCE, SPATIALDI 2024,14619,242-251. |
| MLA | Wang, Peixiao,et al."Predicting Future Spatio-Temporal States Using a Robust Causal Graph Attention Model".SPATIAL DATA AND INTELLIGENCE, SPATIALDI 2024 14619(2024):242-251. |
入库方式： OAI收割
来源：地理科学与资源研究所
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