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Decomposing spatio-temporal heterogeneity: Matrix-informed ensemble learning for interpretable prediction

文献类型：期刊论文

作者Wang, Lizeng1,4; Cheng, Shifen1,4; Lu, Feng1,2,3,4
刊名KNOWLEDGE-BASED SYSTEMS
出版日期2025-01-30
卷号309页码:112906
关键词Spatio-temporal prediction Ensemble learning Spatio-temporal heterogeneity Matrix decomposition Explainable artificial intelligence
ISSN号0950-7051
DOI10.1016/j.knosys.2024.112906
产权排序1
文献子类Article
英文摘要Spatio-temporal prediction aims to forecast location or time-related changes in the physical world with disciplinary knowledge and historical data. Ensemble learning strategies that integrate multiple base learners can leverage the advantages of different models and thus gain wide attention in the field of spatio-temporal prediction. However, existing methods often ignore the unified expression of spatial and temporal heterogeneity in the ensemble process and fail to clarify the mechanisms of model integration under these constraints, limiting the predictive ability and the interpretability of ensemble models. Therefore, this study proposed a Matrix-Informed Ensemble Learning Method (MI-EL) for interpretable spatio-temporal prediction. The core idea of this method is to decompose the spatio-temporal heterogeneous ensemble weight matrix into the multiplication of the spatial and temporal factor matrix. By constructing a spatio-temporal embedding learning module, it utilizes spatial associations and temporal attributes of the samples to solve the spatial and temporal factor matrices, thereby achieving a unified expression of spatial and temporal heterogeneity in the ensemble process. On this basis, interpretable spatial and temporal score vectors are constructed for explicitly expressing the influence intensities and response rules of different base learners under conditions of spatial and temporal heterogeneity. Experiments on traffic flow, traffic speed and air quality prediction tasks show that the proposed method outperforms eight existing ensemble methods in the prediction accuracies at different time steps. Additionally, it effectively identifies the performance patterns of base learners at different spatio-temporal units by assigning higher spatiotemporal scores to better-performing base learners, thereby achieving superior prediction results.
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WOS关键词COMBINATION
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001395012800001
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/211379]  
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Cheng, Shifen
作者单位1.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China;
2.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
3.Fuzhou Univ, Acad Digital China, Fuzhou, Peoples R China;
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
推荐引用方式
GB/T 7714		 Wang, Lizeng,Cheng, Shifen,Lu, Feng. Decomposing spatio-temporal heterogeneity: Matrix-informed ensemble learning for interpretable prediction[J]. KNOWLEDGE-BASED SYSTEMS,2025,309:112906.
APA Wang, Lizeng,Cheng, Shifen,&Lu, Feng.(2025).Decomposing spatio-temporal heterogeneity: Matrix-informed ensemble learning for interpretable prediction.KNOWLEDGE-BASED SYSTEMS,309,112906.
MLA Wang, Lizeng,et al."Decomposing spatio-temporal heterogeneity: Matrix-informed ensemble learning for interpretable prediction".KNOWLEDGE-BASED SYSTEMS 309(2025):112906.

入库方式： OAI收割

来源：地理科学与资源研究所

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