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1 Ergebnisse
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Deep Learning and Statistical Models for Forecasting Transp..:
Fábio Polola Mamede
;
Roberto Fray da Silva
;
Irineu de Brito Junior
...
https://www.mdpi.com/2305-6290/7/4/86. , 2023
Link:
https://doi.org/10.3390/logistics7040086
RT Journal T1
Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers
UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:509873d4768c4f4598e36455ce93c835&Exemplar=1&LAN=DE A1 Fábio Polola Mamede A1 Roberto Fray da Silva A1 Irineu de Brito Junior A1 Hugo Tsugunobu Yoshida Yoshizaki A1 Celso Mitsuo Hino A1 Carlos Eduardo Cugnasca PB MDPI AG YR 2023 K1 transportation demand forecasting K1 supply chain management K1 LSTM K1 ARIMA K1 data preprocessing K1 Transportation and communication K1 K4011-4343 K1 Management. Industrial management K1 HD28-70 K1 Transportation and communications K1 HE1-9990 JF https://www.mdpi.com/2305-6290/7/4/86 LK http://dx.doi.org/https://doi.org/10.3390/logistics7040086 DO https://doi.org/10.3390/logistics7040086 SF ELIB - SuUB Bremen
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