Non-IID spatio-temporal prediction in smart cities
Pages 38 - 41
Abstract
Non-IID spatio-temporal prediction research points toward emerging directions and fundamental solutions to address various complexities from the perspective of both data couplings and heterogeneity. Delving into the non-IID challenge and opportunity of spatio-temporal prediction in smart cities, this article also addresses current solutions to bring some inspiration to future researchers.
References
[1]
Zheng, Y. and Licia, C. et al. Urban computing: Concepts, methodologies, and applications. ACM Transactions on intelligent Systems and Technology 5, 3 (2014), 1--55.
[2]
Longbing, C. Non-IID recommender systems: A review and framework of recommendation paradigm shifting. Engineering 2, 2 (2016), 212--224.
[3]
Jian, S. and Pang, G. et al. Cure: Flexible categorical data representation by hierarchical coupling learning.] IEEE Transactions on Knowledge and Data Engineering 31, 5 (2018,), 853--866.
[4]
Ren, S. and Guo, B. et al. DeepExpress: Heterogeneous and coupled sequence modeling for express delivery prediction. arXiv preprint arXiv:2108.08170, 2021.
Index Terms
- Non-IID spatio-temporal prediction in smart cities
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Published In
Spring 2022
64 pages
ISSN:1528-4972
EISSN:1528-4980
DOI:10.1145/3530850
- Editors:
- Karan Ahuja,
- Ross Teixeira
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 April 2022
Published in XRDS Volume 28, Issue 3
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- Research-article
- Popular
- Refereed
Funding Sources
- the National Key R&D Program of China
- National Science Fund for Distinguished Young Scholars
- the National Natural Science Foundation of China
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