Forecasting worldwide empty container availability with machine learning techniques
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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2364-4575
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Anmerkungen
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online resource.
- Erschienen in
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Forecasting worldwide empty container availability with machine learning techniques ; volume:7 ; number:1 ; day:27 ; month:7 ; year:2022 ; pages:1-24 ; date:12.2022
Journal of shipping and trade ; 7, Heft 1 (27.7.2022), 1-24, 12.2022
- Urheber
- Beteiligte Personen und Organisationen
-
SpringerLink (Online service)
- DOI
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10.1186/s41072-022-00120-x
- URN
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urn:nbn:de:101:1-2022100820424852135618
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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15.08.2025, 07:30 MESZ
Datenpartner
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Beteiligte
- Martius, Christoph
- Kretschmann, Lutz
- Zacharias, Miriam
- Jahn, Carlos
- John, Ole
- SpringerLink (Online service)
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