{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:24:31Z","timestamp":1772792671429,"version":"3.50.1"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319653396","type":"print"},{"value":"9783319653402","type":"electronic"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"tdm","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":[[2017]]},"DOI":"10.1007\/978-3-319-65340-2_46","type":"book-chapter","created":{"date-parts":[[2017,8,8]],"date-time":"2017-08-08T11:49:29Z","timestamp":1502192969000},"page":"561-572","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-objective Learning of Neural Network Time Series Prediction Intervals"],"prefix":"10.1007","author":[{"given":"Pedro Jos\u00e9","family":"Pereira","sequence":"first","affiliation":[]},{"given":"Paulo","family":"Cortez","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Mendes","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,8,9]]},"reference":[{"issue":"4","key":"46_CR1","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1016\/j.eswa.2012.08.018","volume":"40","author":"R Ak","year":"2013","unstructured":"Ak, R., Li, Y., Vitelli, V., Zio, E., Droguett, E.L., Jacinto, C.M.C.: NSGA-II-trained neural network approach to the estimation of prediction intervals of scale deposition rate in oil & gas equipment. Expert Syst. Appl. 40(4), 1205\u20131212 (2013)","journal-title":"Expert Syst. Appl."},{"issue":"11","key":"46_CR2","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1109\/TNNLS.2015.2396933","volume":"26","author":"R Ak","year":"2015","unstructured":"Ak, R., Vitelli, V., Zio, E.: An interval-valued neural network approach for uncertainty quantification in short-term wind speed prediction. IEEE Trans. Neural Netw. Learn. Syst. 26(11), 2787\u20132800 (2015)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"46_CR3","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1109\/TEVC.2009.2015575","volume":"13","author":"N Beume","year":"2009","unstructured":"Beume, N., Fonseca, C.M., L\u00f3pez-Ib\u00e1\u00f1ez, M., Paquete, L., Vahrenhold, J.: On the complexity of computing the hypervolume indicator. IEEE Trans. Evol. Comput. 13(5), 1075\u20131082 (2009)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"46_CR4","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-3-319-47364-2_26","volume-title":"International Joint Conference SOCO\u201916-CISIS\u201916-ICEUTE\u201916","author":"P Cortez","year":"2017","unstructured":"Cortez, P., Matos, L.M., Pereira, P.J., Santos, N., Duque, D.: Forecasting store foot traffic using facial recognition, time series and support vector machines. In: Gra\u00f1a, M., L\u00f3pez-Guede, J.M., Etxaniz, O., Herrero, \u00c1., Quinti\u00e1n, H., Corchado, E. (eds.) ICEUTE\/SOCO\/CISIS -2016. AISC, vol. 527, pp. 267\u2013276. Springer, Cham (2017). doi:10.1007\/978-3-319-47364-2_26"},{"key":"46_CR5","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27, 861\u2013874 (2006)","journal-title":"Pattern Recogn. Lett."},{"key":"46_CR6","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1126\/science.267326","volume":"197","author":"L Glass","year":"1977","unstructured":"Glass, L., Mackey, M.: Oscillation and chaos in physiological control systems. Science 197, 287\u2013289 (1977)","journal-title":"Science"},{"key":"46_CR7","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"T Hastie","year":"2008","unstructured":"Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York (2008)","edition":"2"},{"key":"46_CR8","volume-title":"Nonparametric Statistical Methods","author":"M Hollander","year":"2013","unstructured":"Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods. Wiley, Hoboken (2013)"},{"key":"46_CR9","unstructured":"Hyndman, R.: Time Series Data Library, January 2010. http:\/\/robjhyndman.com\/TSDL\/"},{"issue":"3","key":"46_CR10","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1109\/TNN.2010.2096824","volume":"22","author":"A Khosravi","year":"2011","unstructured":"Khosravi, A., Nahavandi, S., Creighton, D., Atiya, A.F.: Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans. Neural Netw. 22(3), 337\u2013346 (2011)","journal-title":"IEEE Trans. Neural Netw."},{"key":"46_CR11","volume-title":"Forecasting: Methods and Applications","author":"S Makridakis","year":"1998","unstructured":"Makridakis, S., Weelwright, S., Hyndman, R.: Forecasting: Methods and Applications, 3rd edn. Wiley, New York (1998)","edition":"3"},{"key":"46_CR12","volume-title":"Adaptive Business Intelligence","author":"Z Michalewicz","year":"2006","unstructured":"Michalewicz, Z., Schmidt, M., Michalewicz, M., Chiriac, C.: Adaptive Business Intelligence. Springer, Heidelberg (2006)"},{"key":"46_CR13","unstructured":"R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria (2016)"},{"key":"46_CR14","doi-asserted-by":"crossref","unstructured":"Rana, M., Koprinska, I., Khosravi, A., Agelidis, V.G.: Prediction intervals for electricity load forecasting using neural networks. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2013)","DOI":"10.1109\/IJCNN.2013.6706839"},{"issue":"3","key":"46_CR15","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1162\/evco.1994.2.3.221","volume":"2","author":"N Srinivas","year":"1994","unstructured":"Srinivas, N., Deb, K.: Muiltiobjective optimization using nondominated sorting in genetic algorithms. Evol. Comput. 2(3), 221\u2013248 (1994)","journal-title":"Evol. Comput."},{"issue":"6","key":"46_CR16","doi-asserted-by":"publisher","first-page":"1981","DOI":"10.1016\/j.eswa.2012.10.001","volume":"40","author":"M Stepnicka","year":"2013","unstructured":"Stepnicka, M., Cortez, P., Donate, J.P., Stepnickov\u00e1, L.: Forecasting seasonal time series with computational intelligence: on recent methods and the potential of their combinations. Expert Syst. Appl. 40(6), 1981\u20131992 (2013)","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"46_CR17","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/S0169-2070(00)00065-0","volume":"16","author":"L Tashman","year":"2000","unstructured":"Tashman, L.: Out-of-sample tests of forecasting accuracy: an analysis and review. Int. Forecast. J. 16(4), 437\u2013450 (2000)","journal-title":"Int. Forecast. J."}],"container-title":["Lecture Notes in Computer Science","Progress in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-65340-2_46","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T07:53:46Z","timestamp":1772783626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-65340-2_46"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319653396","9783319653402"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-65340-2_46","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"9 August 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"EPIA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"EPIA Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 September 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"epia2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/web.fe.up.pt\/~epia2017\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}