Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162068
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGao, Ruobinen_US
dc.contributor.authorLiu, Jiahuien_US
dc.contributor.authorBai, Xiwenen_US
dc.contributor.authorYuen, Kum Faien_US
dc.date.accessioned2022-10-03T06:32:57Z-
dc.date.available2022-10-03T06:32:57Z-
dc.date.issued2022-
dc.identifier.citationGao, R., Liu, J., Bai, X. & Yuen, K. F. (2022). Annual dilated convolution neural network for newbuilding ship prices forecasting. Neural Computing and Applications, 34(14), 11853-11863. https://dx.doi.org/10.1007/s00521-022-07075-xen_US
dc.identifier.issn0941-0643en_US
dc.identifier.urihttps://hdl.handle.net/10356/162068-
dc.description.abstractAnticipating newbuilding ship prices is crucial for participants in the dynamic shipping market. Although the researchers from forecasting and shipping have shown that the machine learning models outperform statistical ones, convolution neural networks are not investigated. The convolution neural networks are proposed for image processing, rendering difficulty when handling monthly time series. This paper presents a light neural network with annual dilated convolution filters while extracting the newbuilding market’s short-term and long-term temporal knowledge. The multivariate shipping data are fed into multiple convolutional filters with nonlinear activations. Finally, the convoluted features are fed into a linear layer which maps the features to future values. The annual dilated convolution filter owns a vision across one year and integrates all variables’ temporal information. Besides, the dilation rate renders a parsimonious structure, preventing the model from overfitting. The proposed model is compared with statistical models, Naïve forecasts, and various machine learning models on the newbuilding prices of three tanker markets. The empirical results highlight the superiority of the proposed convolutional neural networks.en_US
dc.language.isoenen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.rights© 2022 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. All rights reserved.en_US
dc.subjectEngineering::Maritime studiesen_US
dc.titleAnnual dilated convolution neural network for newbuilding ship prices forecastingen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Civil and Environmental Engineeringen_US
dc.identifier.doi10.1007/s00521-022-07075-x-
dc.identifier.scopus2-s2.0-85126097728-
dc.identifier.issue14en_US
dc.identifier.volume34en_US
dc.identifier.spage11853en_US
dc.identifier.epage11863en_US
dc.subject.keywordsConvolution Neural Networken_US
dc.subject.keywordsShipping Marketen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:CEE Journal Articles

SCOPUSTM   
Citations 50

1
Updated on Jan 28, 2023

Web of ScienceTM
Citations 50

1
Updated on Feb 3, 2023

Page view(s)

21
Updated on Feb 3, 2023

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.