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1 Ergebnisse
1
HierST : A Unified Hierarchical Spatial-temporal Framewo..:
, In:
Proceedings of the 30th ACM International Conference on Information & Knowledge Management
,
Zheng, Shun
;
Gao, Zhifeng
;
Cao, Wei
.. - p. 4383-4392 , 2021
Link:
https://dl.acm.org/doi/10.1145/3459637.3481927
RT T1 <cTitle>Proceedings of the 30th ACM International Conference on Information & Knowledge Management</cTitle>: T1 <Title>HierST : A Unified Hierarchical Spatial-temporal Framework for COVID-19 Trend Forecasting</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3481927&Exemplar=1&LAN=DE A1 Zheng, Shun A1 Gao, Zhifeng A1 Cao, Wei A1 Bian, Jiang A1 Liu, Tie-Yan PB ACM YR 2021 K1 covid-19 K1 graph neural networks K1 spatial-temporal forecasting K1 time-series forecasting K1 Mathematics of computing K1 Probability and statistics K1 Statistical paradigms K1 Time series analysis K1 Applied computing K1 Operations research K1 Forecasting K1 Computing methodologies K1 Machine learning K1 Machine learning approaches K1 Neural networks K1 Information systems K1 Information systems applications K1 Spatial-temporal systems SP 4383 OP 4392 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3459637.3481927 DO https://dl.acm.org/doi/10.1145/3459637.3481927 SF ELIB - SuUB Bremen
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