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  • Search: subject:"Mixture density network"
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Year of publication
Subject
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Machine learning 3 Mixture density network 3 Artificial intelligence 2 Empty container relocation 2 Forecasting model 2 Künstliche Intelligenz 2 Maritime logistics forecasts 2 Prognoseverfahren 2 Algorithm 1 Algorithmus 1 Bayes-Statistik 1 Bayesian inference 1 Container shipping 1 Container terminal 1 Container transport 1 Containerschifffahrt 1 Containerterminal 1 Containerverkehr 1 Distributional forecasting 1 Loss 1 Loss reserving 1 Neural network 1 Neural networks 1 Neuronale Netze 1 Statistical distribution 1 Statistische Verteilung 1 Stochastic process 1 Stochastischer Prozess 1 Theorie 1 Theory 1 Verlust 1
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Online availability
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Free 2 CC license 1 Undetermined 1
Type of publication
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Article 3
Type of publication (narrower categories)
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Article in journal 2 Aufsatz in Zeitschrift 2 Article 1
Language
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English 3
Author
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Jahn, Carlos 2 John, Ole 2 Kretschmann, Lutz 2 Martius, Christoph 2 Zacharias, Miriam 2 Al-Mudafer, Muhammed Taher 1 Avanzi, Benjamin 1 Taylor, Greg 1 Wong, Bernard 1
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Published in...
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Insurance 1 Journal of Shipping and Trade (JST) 1 Journal of shipping and trade 1
Source
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ECONIS (ZBW) 2 EconStor 1
Showing 1 - 3 of 3
Cover Image
Forecasting worldwide empty container availability with machine learning techniques
Martius, Christoph; Kretschmann, Lutz; Zacharias, Miriam; … - In: Journal of shipping and trade 7 (2022), pp. 1-24
Due to imbalances in the global transport of containerised goods, liner shipping companies go to great lengths to match the regional supply and demand for empty containers by transporting equipment from surplus to deficit regions. Making accurate forecasts of regional empty container...
Persistent link: https://www.econbiz.de/10013382290
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Cover Image
Forecasting worldwide empty container availability with machine learning techniques
Martius, Christoph; Kretschmann, Lutz; Zacharias, Miriam; … - In: Journal of Shipping and Trade (JST) 7 (2022) 1, pp. 1-24
Due to imbalances in the global transport of containerised goods, liner shipping companies go to great lengths to match the regional supply and demand for empty containers by transporting equipment from surplus to deficit regions. Making accurate forecasts of regional empty container...
Persistent link: https://www.econbiz.de/10014557463
Saved in:
Cover Image
Stochastic loss reserving with mixture density neural networks
Al-Mudafer, Muhammed Taher; Avanzi, Benjamin; Taylor, Greg - In: Insurance 105 (2022), pp. 144-174
Persistent link: https://www.econbiz.de/10013348990
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