Machine Learning-Based Regression Models for Price Prediction in the Australian Container Shipping Industry: Case Study of Asia-Oceania Trade Lane

Ubaid, A; Hussain, FK; Charles, J

Machine Learning-Based Regression Models for Price Prediction in the Australian Container Shipping Industry: Case Study of Asia-Oceania Trade Lane

Ubaid, A

Hussain, FK Charles, J

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2020, 1151, pp. 52-59
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Ubaid, A https://orcid.org/0000-0002-9833-9891
dc.contributor.author	Hussain, FK
dc.contributor.author	Charles, J
dc.date	2020-04-15
dc.date.accessioned	2021-05-20T02:21:07Z
dc.date.available	2021-05-20T02:21:07Z
dc.date.issued	2020-01-01
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2020, 1151, pp. 52-59
dc.identifier.isbn	9783030440404
dc.identifier.issn	2194-5357
dc.identifier.issn	2194-5365
dc.identifier.uri	http://hdl.handle.net/10453/148999
dc.description.abstract	The objective of this paper is to train a data-driven price prediction model for container pricing based on demand and supply for the Australian container shipping industry. The sourcing of demand, supply and pricing data has been done from Australian ports, Sea-Intelligence maritime analysis and the Shanghai Freight Index (SCFI) respectively. Data-driven prediction have been realized by applying three different regression models that include support vector regression (SVR), random forest regression (RFR) and gradient booster regression (GBR) over the gathered datasets after initial feature engineering. A comparison of research outcomes shows that GBR outperforms all the other models by offering a test accuracy of 84%.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Advances in Intelligent Systems and Computing
dc.relation.ispartof	International Conference on Advanced Information Networking and Applications
dc.relation.ispartofseries	Advances in Intelligent Systems and Computing
dc.relation.isbasedon	10.1007/978-3-030-44041-1_5
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Machine Learning-Based Regression Models for Price Prediction in the Australian Container Shipping Industry: Case Study of Asia-Oceania Trade Lane
dc.type	Conference Proceeding
utslib.citation.volume	1151
utslib.location.activity	Italy
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2021-05-20T02:21:04Z
pubs.finish-date	2020-04-17
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2020-04-15
pubs.volume	1151
dc.location	Switzerland

Abstract:

The objective of this paper is to train a data-driven price prediction model for container pricing based on demand and supply for the Australian container shipping industry. The sourcing of demand, supply and pricing data has been done from Australian ports, Sea-Intelligence maritime analysis and the Shanghai Freight Index (SCFI) respectively. Data-driven prediction have been realized by applying three different regression models that include support vector regression (SVR), random forest regression (RFR) and gradient booster regression (GBR) over the gathered datasets after initial feature engineering. A comparison of research outcomes shows that GBR outperforms all the other models by offering a test accuracy of 84%.

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http://hdl.handle.net/10453/148999