{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T09:52:40Z","timestamp":1785923560653,"version":"3.56.0"},"reference-count":127,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Xi\u2019an Jiaotong-Liverpool University","award":["RDF-22-02-026"],"award-info":[{"award-number":["RDF-22-02-026"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The maritime industry is integral to global trade and heavily depends on precise forecasting to maintain efficiency, safety, and economic sustainability. Adopting deep learning for predictive analysis has markedly improved operational accuracy, cost efficiency, and decision-making. This technology facilitates advanced time series analysis, vital for optimizing maritime operations. This paper reviews deep learning applications in time series analysis within the maritime industry, focusing on three areas: ship operation-related, port operation-related, and shipping market-related topics. It provides a detailed overview of the existing literature on applications such as ship trajectory prediction, ship fuel consumption prediction, port throughput prediction, and shipping market prediction. The paper comprehensively examines the primary deep learning architectures used for time series forecasting in the maritime industry, categorizing them into four principal types. It systematically analyzes the advantages of deep learning architectures across different application scenarios and explores methodologies for selecting models based on specific requirements. Additionally, it analyzes data sources from the existing literature and suggests future research directions.<\/jats:p>","DOI":"10.3390\/info15080507","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:53:19Z","timestamp":1724417599000},"page":"507","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Advancements in Deep Learning Techniques for Time Series Forecasting in Maritime Applications: A Comprehensive Review"],"prefix":"10.3390","volume":"15","author":[{"given":"Meng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Intelligent Finance and Business, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-5199-7026","authenticated-orcid":false,"given":"Xinyan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Intelligent Finance and Business, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanling","family":"She","sequence":"additional","affiliation":[{"name":"Faculty of International Tourism and Management, City University of Macau, Avenida Padre Tom\u2019as Pereira Taipa, Macau 999078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Zhejiang \u201cEight Eight Strategy\u201d Innovation and Development Research Institute, The Party School of Zhejiang Provincial Committee of the Communist Party of China, Hangzhou 311121, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maohan","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5723-2265","authenticated-orcid":false,"given":"Zhong Shuo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Intelligent Finance and Business, Xi\u2019an Jiaotong-Liverpool University, Suzhou 215400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"ref_1","unstructured":"UNCTAD (2023). 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