{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T21:16:26Z","timestamp":1743023786496,"version":"3.40.3"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319941196"},{"type":"electronic","value":"9783319941202"}],"license":[{"start":{"date-parts":[[2018,6,7]],"date-time":"2018-06-07T00:00:00Z","timestamp":1528329600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-319-94120-2_34","type":"book-chapter","created":{"date-parts":[[2018,6,6]],"date-time":"2018-06-06T04:02:46Z","timestamp":1528257766000},"page":"357-366","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SVR-Ensemble Forecasting Approach for Ro-Ro Freight at Port of Algeciras (Spain)"],"prefix":"10.1007","author":[{"given":"Jose Antonio","family":"Moscoso-L\u00f3pez","sequence":"first","affiliation":[]},{"given":"Ignacio J.","family":"Turias","sequence":"additional","affiliation":[]},{"given":"Juan Jes\u00fas Ruiz","family":"Aguilar","sequence":"additional","affiliation":[]},{"given":"Francisco Javier","family":"Gonzalez-Enrique","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,6,7]]},"reference":[{"key":"34_CR1","first-page":"22","volume":"13","author":"CI Bilegan","year":"2008","unstructured":"Bilegan, C.I., Crainic, T.G., Gendreau, M.: Forecasting freight demand at intermodal terminals using neural networks\u2013an integrated framework. Eur. J. Oper. Res. 13, 22\u201336 (2008)","journal-title":"Eur. J. Oper. Res."},{"key":"34_CR2","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1111\/itor.12021","volume":"20","author":"G Romero","year":"2013","unstructured":"Romero, G., Dur\u00e1n, G., Marenco, J., Weintraub, A.: An approach for efficient ship routing. Int. Trans. Oper. Res. 20, 767\u2013794 (2013)","journal-title":"Int. Trans. Oper. Res."},{"key":"34_CR3","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1080\/0144164042000195072","volume":"24","author":"EI Vlahogianni","year":"2004","unstructured":"Vlahogianni, E.I., Golias, J.C., Karlaftis, M.G.: Short-term traffic forecasting: overview of objectives and methods. Transp. Rev. 24, 533\u2013557 (2004)","journal-title":"Transp. Rev."},{"key":"34_CR4","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3141\/1763-14","volume":"1763","author":"HM Al-Deek","year":"2001","unstructured":"Al-Deek, H.M.: Which method is better for developing freight planning models at seaports - neural networks or multiple regression? Transp. Res. Rec. 1763, 90\u201397 (2001)","journal-title":"Transp. Res. Rec."},{"key":"34_CR5","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.jtrangeo.2003.12.003","volume":"12","author":"H Murat Celik","year":"2004","unstructured":"Murat Celik, H.: Modeling freight distribution using artificial neural networks. J. Transp. Geogr. 12, 141\u2013148 (2004)","journal-title":"J. Transp. Geogr."},{"key":"34_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1080\/0308883032000174463","volume":"31","author":"MM Mostafa","year":"2004","unstructured":"Mostafa, M.M.: Forecasting the Suez Canal traffic: a neural network analysis. Marit. Policy Manag. 31, 139\u2013156 (2004)","journal-title":"Marit. Policy Manag."},{"key":"34_CR7","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.procs.2014.05.426","volume":"32","author":"NT Ratrout","year":"2014","unstructured":"Ratrout, N.T., Gazder, U.: Factors affecting performance of parametric and non-parametric models for daily traffic forecasting. Procedia Comput. Sci. 32, 285\u2013292 (2014)","journal-title":"Procedia Comput. Sci."},{"key":"34_CR8","volume-title":"Statistical Learning Theory","author":"V Vapnik","year":"1998","unstructured":"Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)"},{"key":"34_CR9","doi-asserted-by":"publisher","first-page":"2979","DOI":"10.1016\/j.eswa.2008.01.073","volume":"36","author":"M Castro-Neto","year":"2009","unstructured":"Castro-Neto, M., Jeong, Y., Jeong, M.K., Han, L.D.: AADT prediction using support vector regression with data-dependent parameters. Expert Syst. Appl. 36, 2979\u20132986 (2009)","journal-title":"Expert Syst. Appl."},{"key":"34_CR10","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.trc.2013.10.012","volume":"38","author":"A Bhattacharya","year":"2014","unstructured":"Bhattacharya, A., Kumar, S.A., Tiwari, M., Talluri, S.: An intermodal freight transport system for optimal supply chain logistics. Transp. Res. Part C Emerg. Technol. 38, 73\u201384 (2014)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"34_CR11","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1111\/itor.12098","volume":"21","author":"W-Y Hwang","year":"2014","unstructured":"Hwang, W.-Y., Lee, J.-S.: A new forecasting scheme for evaluating long-term prediction performances in supply chain management. Int. Trans. Oper. Res. 21, 1045\u20131060 (2014)","journal-title":"Int. Trans. Oper. Res."},{"key":"34_CR12","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1016\/j.trc.2015.04.004","volume":"56","author":"N Markovi\u0107","year":"2015","unstructured":"Markovi\u0107, N., Milinkovi\u0107, S., Tikhonov, K.S., Schonfeld, P.: Analyzing passenger train arrival delays with support vector regression. Transp. Res. Part C Emerg. Technol. 56, 251\u2013262 (2015)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"34_CR13","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.trc.2010.10.004","volume":"19","author":"MG Karlaftis","year":"2011","unstructured":"Karlaftis, M.G., Vlahogianni, E.I.: Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transp. Res. Part C Emerg. Technol. 19, 387\u2013399 (2011)","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"34_CR14","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.1016\/j.mcm.2009.05.027","volume":"50","author":"WY Peng","year":"2009","unstructured":"Peng, W.Y., Chu, C.W.: A comparison of univariate methods for forecasting container throughput volumes. Math. Comput. Model. 50, 1045\u20131057 (2009)","journal-title":"Math. Comput. Model."},{"key":"34_CR15","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1080\/03081060.2013.851506","volume":"36","author":"X Tian","year":"2013","unstructured":"Tian, X., Liu, L., Lai, K.K., Wang, S.: Analysis and forecasting of port logistics using TEI@I methodology. Transp. Plan. Technol. 36, 669\u2013684 (2013)","journal-title":"Transp. Plan. Technol."},{"key":"34_CR16","doi-asserted-by":"publisher","first-page":"2232","DOI":"10.1016\/j.asoc.2013.02.002","volume":"13","author":"G Xie","year":"2013","unstructured":"Xie, G., Wang, S., Zhao, Y., Lai, K.K.: Hybrid approaches based on LSSVR model for container throughput forecasting: a comparative study. Appl. Soft Comput. 13, 2232\u20132241 (2013)","journal-title":"Appl. Soft Comput."},{"key":"34_CR17","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.neucom.2014.06.070","volume":"147","author":"J Geng","year":"2015","unstructured":"Geng, J., Li, M.-W., Dong, Z.-H., Liao, Y.-S.: Port throughput forecasting by MARS-RSVR with chaotic simulated annealing particle swarm optimization algorithm. Neurocomputing. 147, 239\u2013250 (2015)","journal-title":"Neurocomputing."},{"key":"34_CR18","doi-asserted-by":"publisher","first-page":"2119","DOI":"10.1080\/00207543.2014.965852","volume":"53","author":"JJ Ruiz-Aguilar","year":"2015","unstructured":"Ruiz-Aguilar, J.J., Turias, I.J., Jim\u00e9nez-Come, M.J.: A two-stage procedure for forecasting freight inspections at Border Inspection Posts using SOMs and support vector regression. Int. J. Prod. Res. 53, 2119\u20132130 (2015)","journal-title":"Int. J. Prod. Res."},{"key":"34_CR19","volume-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"B Sch\u00f6lkopf","year":"2002","unstructured":"Sch\u00f6lkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2002)"},{"key":"34_CR20","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ijforecast.2015.07.002","volume":"32","author":"C Bergmeir","year":"2016","unstructured":"Bergmeir, C., Hyndman, R.J., Ben\u00edtez, J.M.: Bagging exponential smoothing methods using STL decomposition and Box-Cox transformation. Int. J. Forecast. 32, 303\u2013312 (2016)","journal-title":"Int. J. Forecast."},{"key":"34_CR21","volume-title":"Statistical Methods and Scientific Inference","author":"RA Fisher","year":"1956","unstructured":"Fisher, R.A.: Statistical Methods and Scientific Inference. Hafner Publishing Co, Oxford (1956)"},{"key":"34_CR22","doi-asserted-by":"publisher","first-page":"4235","DOI":"10.1016\/j.eswa.2013.12.011","volume":"41","author":"N Kourentzes","year":"2014","unstructured":"Kourentzes, N., Barrow, D.K., Crone, S.F.: Neural network ensemble operators for time series forecasting. Expert Syst. Appl. 41, 4235\u20134244 (2014)","journal-title":"Expert Syst. Appl."},{"key":"34_CR23","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/BF00116037","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5, 197\u2013227 (1990)","journal-title":"Mach. Learn."},{"key":"34_CR24","unstructured":"Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of the ICML, pp. 148\u2013156 (1996)"},{"key":"34_CR25","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24, 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"key":"34_CR26","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1007\/978-3-319-07013-1_33","volume-title":"Proceedings of the Ninth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX. June 30 \u2013 July 4, 2014, Brun\u00f3w, Poland","author":"Jos\u00e9 Antonio Moscoso L\u00f3pez","year":"2014","unstructured":"Moscoso-L\u00f3pez, J.A., Ruiz-Aguilar, J.J., Turias, I., Cerb\u00e1n, M., Jim\u00e9nez-Come, M.J.: A comparison of forecasting methods for ro-ro traffic: a case study in the strait of gibraltar. In: Proceedings of the Ninth International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, 30 June\u20134 July, 2014, Brun\u00f3w, Poland, pp. 345\u2013353. Springer (2014)"},{"key":"34_CR27","doi-asserted-by":"crossref","unstructured":"Ruiz-Aguilar, J.J., Turias, I.J., Moscoso-L\u00f3pez, J.A., Come, M.J.J., Cerb\u00e1n, M.M.: Forecasting of short-term flow freight congestion: A study case of Algeciras Bay Port (Spain) (2016). http:\/\/www.revistas.unal.edu.co\/index.php\/dyna\/article\/view\/47027","DOI":"10.15446\/dyna.v83n195.47027"}],"container-title":["Advances in Intelligent Systems and Computing","International Joint Conference SOCO\u201918-CISIS\u201918-ICEUTE\u201918"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-94120-2_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,19]],"date-time":"2024-10-19T16:04:09Z","timestamp":1729353849000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-94120-2_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,7]]},"ISBN":["9783319941196","9783319941202"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-94120-2_34","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2018,6,7]]},"assertion":[{"value":"7 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SOCO\u201918-CISIS\u201918-ICEUTE\u201918","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The 13th International Conference on Soft Computing Models in Industrial and Environmental Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"san sebastian","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 June 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icscmiea2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.soco2018.eu","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}