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Henry S. Marcus. Voudris, Athanasios V Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2007-02-21T13:18:55Z 2007-02-21T13:18:55Z 2006 2006 http://hdl.handle.net/1721.1/36269 77464366 Thesis (S.M. in Ocean Systems Management)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006. Includes bibliographical references (leaves 188-189). Investing in the bulk carrier market constitutes a rather risky investment due to the volatility of the bulk carrier freight rates. In this study it is attempted to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting the Capesize Ore Voyage Rates from Tubarao to Rotterdam with a 145,000 dwt Bulk carrier. Initially, market analysis allows the assessment of the relation of some parameters of the dry bulk market with the evolution of freight rates. Subsequently, ANNs with an appropriate architecture are constructed and sufficient data, in terms of quantity and quality, are collected and organized so as to establish both the training and the testing data sets. The use of ANNs along with genetic algorithms allows the prediction of bulk freight rates with considerable accuracy for as long as eighteen months ahead and this is quantified by calculating the relative and absolute errors. It is concluded that ANNs offer a promising approach to forecasting the bulk market when coupled with efficient market modeling. by Athanasios V. Voudris. S.M. in Ocean Systems Management 197 leaves eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Mechanical Engineering. Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6cf5515f-012e-4b08-9093-1102d0de906b"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Analysis and forecast of the capesize bulk carriers shipping market using Artificial Neural Networks</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2006</PublicationDate> <Authors> <Author> <DisplayName>Voudris, Athanasios V</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <Abstract>Investing in the bulk carrier market constitutes a rather risky investment due to the volatility of the bulk carrier freight rates. In this study it is attempted to uncover the benefits of using Artificial Neural Networks (ANNs) in forecasting the Capesize Ore Voyage Rates from Tubarao to Rotterdam with a 145,000 dwt Bulk carrier. Initially, market analysis allows the assessment of the relation of some parameters of the dry bulk market with the evolution of freight rates. Subsequently, ANNs with an appropriate architecture are constructed and sufficient data, in terms of quantity and quality, are collected and organized so as to establish both the training and the testing data sets. The use of ANNs along with genetic algorithms allows the prediction of bulk freight rates with considerable accuracy for as long as eighteen months ahead and this is quantified by calculating the relative and absolute errors. It is concluded that ANNs offer a promising approach to forecasting the bulk market when coupled with efficient market modeling.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>