{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T08:15:16Z","timestamp":1726042516703},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030298586"},{"type":"electronic","value":"9783030298593"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-29859-3_37","type":"book-chapter","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T16:03:53Z","timestamp":1566835433000},"page":"431-443","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Forecast Daily Air-Pollution Time Series with Deep Learning"],"prefix":"10.1007","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-4445-8868","authenticated-orcid":false,"given":"Miguel","family":"C\u00e1rdenas-Montes","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,8,26]]},"reference":[{"key":"37_CR1","unstructured":"Open data Madrid, August 2018. https:\/\/datos.madrid.es\/portal\/site\/egob"},{"issue":"8","key":"37_CR2","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1007\/s004200050321","volume":"71","author":"JC Alberdi Odriozola","year":"1998","unstructured":"Alberdi Odriozola, J.C., D\u00edaz Jim\u00e9nez, J., Montero Rubio, J.C., Mir\u00f3n P\u00e9rez, I.J., Pajares Ort\u00edz, M.S., Ribera Rodrigues, P.: Air pollution and mortality in Madrid, Spain: a time-series analysis. Int. Arch. Occup. Environ. Health 71(8), 543\u2013549 (1998). https:\/\/doi.org\/10.1007\/s004200050321","journal-title":"Int. Arch. Occup. Environ. Health"},{"key":"37_CR3","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198538493.001.0001","volume-title":"Neural Networks for Pattern Recognition","author":"CM Bishop","year":"1995","unstructured":"Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press Inc., New York (1995)"},{"key":"37_CR4","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1016\/j.scitotenv.2018.03.149","volume":"635","author":"R Borge","year":"2018","unstructured":"Borge, R., et al.: Application of a short term air quality action plan in Madrid (Spain) under a high-pollution episode - part I: Diagnostic and analysis from observations. Sci. Total Environ. 635, 1561\u20131573 (2018). https:\/\/doi.org\/10.1016\/j.scitotenv.2018.03.149","journal-title":"Sci. Total Environ."},{"key":"37_CR5","unstructured":"Chollet, F., et al.: Keras (2015). https:\/\/github.com\/fchollet\/keras"},{"key":"37_CR6","first-page":"3","volume":"6","author":"RB Cleveland","year":"1990","unstructured":"Cleveland, R.B., Cleveland, W.S., McRae, J., Terpenning, I.: STL: a seasonal-trend decomposition procedure based on loess. J. Off. Statist. 6, 3\u201373 (1990)","journal-title":"J. Off. Statist."},{"issue":"6","key":"37_CR7","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1007\/s004200050388","volume":"72","author":"J D\u00edaz","year":"1999","unstructured":"D\u00edaz, J., et al.: Modeling of air pollution and its relationship with mortality and morbidity in Madrid, Spain. Int. Arch. Occup. Environ. Health 72(6), 366\u2013376 (1999). https:\/\/doi.org\/10.1007\/s004200050388","journal-title":"Int. Arch. Occup. Environ. Health"},{"issue":"10","key":"37_CR8","doi-asserted-by":"publisher","first-page":"959","DOI":"10.1007\/s00500-008-0392-y","volume":"13","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda, S., Fern\u00e1ndez, A., Luengo, J., Herrera, F.: A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft. Comput. 13(10), 959\u2013977 (2009)","journal-title":"Soft. Comput."},{"issue":"6","key":"37_CR9","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1007\/s10732-008-9080-4","volume":"15","author":"S Garc\u00eda","year":"2009","unstructured":"Garc\u00eda, S., Molina, D., Lozano, M., Herrera, F.: A study on the use of non-parametric tests for analyzing the evolutionary algorithms\u2019 behaviour: a case study on the CEC\u20192005 special session on real parameter optimization. J. Heuristics 15(6), 617\u2013644 (2009)","journal-title":"J. Heuristics"},{"key":"37_CR10","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press (2016). http:\/\/www.deeplearningbook.org"},{"issue":"8","key":"37_CR11","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997). https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput."},{"key":"37_CR12","unstructured":"LeCun, Y.: Generalization and network design strategies. Technical report. University of Toronto (1989)"},{"issue":"2","key":"37_CR13","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1007\/s00420-005-0032-0","volume":"79","author":"C Linares","year":"2006","unstructured":"Linares, C., D\u00edaz, J., Tob\u00edas, A., Miguel, J.M.D., Otero, A.: Impact of urban air pollutants and noise levels over daily hospital admissions in children in Madrid: a time series analysis. Int. Arch. Occup. Environ. Health 79(2), 143\u2013152 (2006). https:\/\/doi.org\/10.1007\/s00420-005-0032-0","journal-title":"Int. Arch. Occup. Environ. Health"},{"key":"37_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/978-3-030-00374-6_9","volume-title":"Advances in Artificial Intelligence","author":"I M\u00e9ndez-Jim\u00e9nez","year":"2018","unstructured":"M\u00e9ndez-Jim\u00e9nez, I., C\u00e1rdenas-Montes, M.: Time series decomposition for improving the forecasting performance of convolutional neural networks. In: Herrera, F., et al. (eds.) CAEPIA 2018. LNCS (LNAI), vol. 11160, pp. 87\u201397. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-00374-6_9"},{"issue":"11","key":"37_CR15","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster, M., Paliwal, K.: Bidirectional recurrent neural networks. Trans. Sig. Proc. 45(11), 2673\u20132681 (1997). https:\/\/doi.org\/10.1109\/78.650093","journal-title":"Trans. Sig. Proc."}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-29859-3_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:29:37Z","timestamp":1710268177000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-29859-3_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030298586","9783030298593"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-29859-3_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"26 August 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HAIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Artificial Intelligence Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Le\u00f3n","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":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"hais2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2019.haisconference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"134","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":"64","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":"0","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":"48% - 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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}