{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T11:35:50Z","timestamp":1772796950250,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643680200","type":"print"},{"value":"9781643680217","type":"electronic"}],"license":[{"start":{"date-parts":[[2019,10,7]],"date-time":"2019-10-07T00:00:00Z","timestamp":1570406400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,7]]},"abstract":"<jats:p>The Baltic Dry Index (BDI) is an indicator of freight rates of dry bulk cargo. Since freight rate fluctuates widely with reflecting a rapid change in the shipping market, it is important for a shipping company to predict the BDI. In the shipping industry, detailed data on the movement of each vessel is currently available in the automated identification system (AIS) on board, and a wide use of the AIS data is increasingly expected. This study proposes a new prediction method for freight index using deep learning and AIS data. The prediction target is the Baltic Capesize Index (BCI) representing the freight rate index of a large dry-bulk carrier over 180,000 DWT. The AIS data and various statistics are incorporated into the model to predict the rise or fall of the BCI value after 30 days. A multiple set of AIS data of the entire world and several specific regions are used. Furthermore, the number of related statistics to be incorporated is increased and a method of selecting them by introducing maximal information coefficient is shown. From the simulation results, the prediction of BCI can be performed with a certain degree of accuracy. In addition, the effect of introducing AIS data in the BCI prediction is confirmed.<\/jats:p>","DOI":"10.3233\/atde190113","type":"book-chapter","created":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T09:02:20Z","timestamp":1635152540000},"source":"Crossref","is-referenced-by-count":6,"title":["Predicting a Dry Bulk Freight Index by Deep Learning with Global Vessel Movement Data"],"prefix":"10.3233","author":[{"given":"Kei","family":"Kanamoto","sequence":"first","affiliation":[{"name":"Department of Systems Innovation, the University of Tokyo, Japan"}]},{"given":"Yujiro","family":"Wada","sequence":"additional","affiliation":[{"name":"Graduate School of Engineering, Hiroshima University, Japan"}]},{"given":"Ryuichi","family":"Shibasaki","sequence":"additional","affiliation":[{"name":"Department of Systems Innovation, the University of Tokyo, Japan"}]}],"member":"7437","container-title":["Advances in Transdisciplinary Engineering","Transdisciplinary Engineering for Complex Socio-technical Systems"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/ATDE190113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,25]],"date-time":"2021-10-25T09:02:21Z","timestamp":1635152541000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/ATDE190113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,7]]},"ISBN":["9781643680200","9781643680217"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/atde190113","relation":{},"ISSN":["2352-751X","2352-7528"],"issn-type":[{"value":"2352-751X","type":"print"},{"value":"2352-7528","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,10,7]]}}}