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1 Ergebnisse
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Forecasting daily COVID-19 cases with gradient boosted regr..:
Anindya Sen
;
Nathaniel T. Stevens
;
N. Ken Tran
...
https://www.frontiersin.org/articles/10.3389/fpubh.2023.1259410/full. , 2023
Link:
https://doi.org/10.3389/fpubh.2023.1259410
RT Journal T1 <Title>Forecasting daily COVID-19 cases with gradient boosted regression trees and other methods: evidence from U.S. cities</Title> UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:c9061bb31e0a4dd6a5b85ec3c3ca5847&Exemplar=1&LAN=DE A1 Anindya Sen A1 Nathaniel T. Stevens A1 N. Ken Tran A1 Rishav R. Agarwal A1 Qihuang Zhang A1 Joel A. Dubin PB Frontiers Media S.A. YR 2023 K1 daily COVID-19 cases K1 epidemiological surveillance K1 Metropolitan Statistical Areas K1 Gradient Boosted Regression Trees K1 Seasonal Autoregressive Integrated Moving Average (SARIMA) K1 Susceptible K1 Public aspects of medicine K1 RA1-1270 JF https://www.frontiersin.org/articles/10.3389/fpubh.2023.1259410/full LK http://dx.doi.org/https://doi.org/10.3389/fpubh.2023.1259410 DO https://doi.org/10.3389/fpubh.2023.1259410 SF ELIB - SuUB Bremen
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