Adding attention to the neural ordinary differential equation for spatio-temporal prediction-Reference-Cited by-同舟云学术

Adding attention to the neural ordinary differential equation for spatio-temporal prediction

Published:2023-11-07 Issue:1 Volume:38 Page:156-181
ISSN:1365-8816
Container-title:International Journal of Geographical Information Science
language:en
Short-container-title:International Journal of Geographical Information Science

Author:

Wang Peixiao¹²³^ORCID,Zhang Tong²^ORCID,Zhang Hengcai¹³^ORCID,Cheng Shifen¹^ORCID,Wang Wangshu⁴^ORCID

Affiliation:

1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China

3. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

4. Research Unit Cartography, Department of Geodesy and Geoinformation, Vienna University of Technology, Vienna, Austria

Funder

National Key Research and Development Program of China

National Postdoctoral Innovation Talents Support Program

Wuhan University-Huawei Geoinformatics Innovation Laboratory

National Natural Science Foundation of China

Special Research Assistant Program of Chinese Academy of Sciences, Innovation Project of LREIS

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Link

https://www.tandfonline.com/doi/pdf/10.1080/13658816.2023.2275160

Reference47 articles.

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2. Multivariate interpolation to incorporate thematic surface data using inverse distance weighting (IDW)

3. Chen, R.T.Q., et al., 2018. Neural ordinary differential equations. Proceedings of the 32nd International Conference on Neural Information Processing Systems, 6572–6583.

4. Bayesian temporal factorization for multidimensional time series prediction;Chen X.;IEEE Transactions on Pattern Analysis and Machine Intelligence,2022

5. Short-Term Traffic Forecasting by Mining the Non-Stationarity of Spatiotemporal Patterns

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