{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:18:34Z","timestamp":1742912314802,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031001253"},{"type":"electronic","value":"9783031001260"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-00126-0_42","type":"book-chapter","created":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:07:55Z","timestamp":1650996475000},"page":"590-605","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Port Container Throughput Prediction Based on Variational AutoEncoder"],"prefix":"10.1007","author":[{"given":"Jingze","family":"Li","sequence":"first","affiliation":[]},{"given":"Shengmin","family":"Shi","sequence":"additional","affiliation":[]},{"given":"Tongbing","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Tian","sequence":"additional","affiliation":[]},{"given":"Yihua","family":"Ding","sequence":"additional","affiliation":[]},{"given":"Yiyong","family":"Xiao","sequence":"additional","affiliation":[]},{"given":"Weiwei","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,8]]},"reference":[{"issue":"2","key":"42_CR1","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1109\/72.279188","volume":"5","author":"JT Connor","year":"1994","unstructured":"Connor, J.T., Martin, R.D., Atlas, L.E.: Recurrent neural networks and robust time series prediction. IEEE Trans. Neural Netw. 5(2), 240\u2013254 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"key":"42_CR2","first-page":"1","volume":"3","author":"M Eskafi","year":"2021","unstructured":"Eskafi, M., Kowsari, M., Dastgheib, A., Ulfarsson, G.F., Thorarinsdottir, R.I.: A model for port throughput forecasting using Bayesian estimation. Marit. Econ. Logist. 3, 1\u201321 (2021)","journal-title":"Marit. Econ. Logist."},{"key":"42_CR3","doi-asserted-by":"crossref","unstructured":"Friedman, J.H.: Greedy function approximation: a gradient boosting machine. Ann. Stat., 1189\u20131232 (2001)","DOI":"10.1214\/aos\/1013203451"},{"key":"42_CR4","unstructured":"Huang, Z., Xu, W., Yu, K.: Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991 (2015)"},{"key":"42_CR5","doi-asserted-by":"crossref","unstructured":"Li, L., et al.: Using improved gradient-boosted decision tree algorithm based on Kalman filter (GBDT-KF) in time series prediction. J. Supercomput., 1\u201314 (2020)","DOI":"10.1007\/s11227-019-03130-y"},{"issue":"6","key":"42_CR6","doi-asserted-by":"publisher","first-page":"600","DOI":"10.7470\/jkst.2014.32.6.600","volume":"32","author":"KC Min","year":"2014","unstructured":"Min, K.C., Ha, H.K.: Forecasting the Korea\u2019s port container volumes with Sarima model. J. Korean Soc. Transp. 32(6), 600\u2013614 (2014)","journal-title":"J. Korean Soc. Transp."},{"key":"42_CR7","doi-asserted-by":"publisher","first-page":"478","DOI":"10.1016\/j.asoc.2017.10.033","volume":"62","author":"L Mo","year":"2018","unstructured":"Mo, L., Xie, L., Jiang, X., Teng, G., Xu, L., Xiao, J.: GMDH-based hybrid model for container throughput forecasting: selective combination forecasting in nonlinear subseries. Appl. Soft Comput. 62, 478\u2013490 (2018)","journal-title":"Appl. Soft Comput."},{"key":"42_CR8","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.apm.2018.01.014","volume":"57","author":"M Niu","year":"2018","unstructured":"Niu, M., Hu, Y., Sun, S., Liu, Y.: A novel hybrid decomposition-ensemble model based on VMD and HGWO for container throughput forecasting. Appl. Math. Modelling 57, 163\u2013178 (2018)","journal-title":"Appl. Math. Modelling"},{"issue":"4","key":"42_CR9","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1057\/mel.2016.8","volume":"19","author":"Y Rashed","year":"2017","unstructured":"Rashed, Y., Meersman, H., Van de Voorde, E., Vanelslander, T.: Short-term forecast of container throughout: an ARIMA-intervention model for the port of Antwerp. Marit. Econ. Logist. 19(4), 749\u2013764 (2017)","journal-title":"Marit. Econ. Logist."},{"key":"42_CR10","doi-asserted-by":"crossref","unstructured":"Su, H., Zhang, L., Yu, S.: Short-term traffic flow prediction based on incremental support vector regression. In: Third International Conference on Natural Computation (ICNC 2007), vol. 1, pp. 640\u2013645. IEEE (2007)","DOI":"10.1109\/ICNC.2007.661"},{"key":"42_CR11","unstructured":"Vaswani, A., et al.: Attention is all you need. arXiv preprint arXiv:1706.03762 (2017)"},{"issue":"10","key":"42_CR12","doi-asserted-by":"publisher","first-page":"1784","DOI":"10.3390\/math8101784","volume":"8","author":"CH Yang","year":"2020","unstructured":"Yang, C.H., Chang, P.Y.: Forecasting the demand for container throughput using a mixed-precision neural architecture based on CNN-LSTM. Mathematics 8(10), 1784 (2020)","journal-title":"Mathematics"},{"key":"42_CR13","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Fu, Y., Li, G.: Research on container throughput forecast based on ARIMA-BP neural network. In: Journal of Physics: Conference Series (2020)","DOI":"10.1088\/1742-6596\/1634\/1\/012024"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-00126-0_42","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,4,26]],"date-time":"2022-04-26T18:15:27Z","timestamp":1650996927000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-00126-0_42"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031001253","9783031001260"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-00126-0_42","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"8 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.dasfaa2022.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"543","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"72","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"76","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"13% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"6","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was originally planned to take place in Hyberabad, India. 24 other papers are included in the volume.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}