{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T09:07:04Z","timestamp":1743152824208,"version":"3.40.3"},"publisher-location":"Cham","reference-count":13,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030034955"},{"type":"electronic","value":"9783030034962"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-030-03496-2_21","type":"book-chapter","created":{"date-parts":[[2018,11,8]],"date-time":"2018-11-08T12:58:05Z","timestamp":1541681885000},"page":"180-187","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Wind Power Ramp Events Ordinal Prediction Using Minimum Complexity Echo State Networks"],"prefix":"10.1007","author":[{"given":"M.","family":"Dorado-Moreno","sequence":"first","affiliation":[]},{"given":"P. A.","family":"Guti\u00e9rrez","sequence":"additional","affiliation":[]},{"given":"S.","family":"Salcedo-Sanz","sequence":"additional","affiliation":[]},{"given":"L.","family":"Prieto","sequence":"additional","affiliation":[]},{"given":"C.","family":"Herv\u00e1s-Mart\u00ednez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2018,11,9]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Baccianella, S., Esuli, A., Sebastiani, F.: Evaluation measures for ordinal regression. In: Proceedings of the Ninth International Conference on Intelligent Systems Design and Applications, pp. 283\u2013287 (2009)","DOI":"10.1109\/ISDA.2009.230"},{"key":"21_CR2","series-title":"Advances in Intelligent Systems and Computing","doi-asserted-by":"publisher","first-page":"475","DOI":"10.1007\/978-3-319-07776-5_49","volume-title":"Intelligent Data analysis and its Applications, Volume I","author":"S Basterrech","year":"2014","unstructured":"Basterrech, S., Buri\u00e1nek, T.: Solar irradiance estimation using the echo state network and the flexible neural tree. In: Pan, J.-S., Snasel, V., Corchado, E.S., Abraham, A., Wang, S.-L. (eds.) Intelligent Data analysis and its Applications, Volume I. AISC, vol. 297, pp. 475\u2013484. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-07776-5_49"},{"key":"21_CR3","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artif. Intell. Res."},{"key":"21_CR4","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"300","DOI":"10.1007\/978-3-319-44636-3_28","volume-title":"Advances in Artificial Intelligence","author":"M Dorado-Moreno","year":"2016","unstructured":"Dorado-Moreno, M., et al.: Multiclass prediction of wind power ramp events combining reservoir computing and support vector machines. In: Luaces, O., G\u00e1mez, J.A., Barrenechea, E., Troncoso, A., Galar, M., Quinti\u00e1n, H., Corchado, E. (eds.) CAEPIA 2016. LNCS (LNAI), vol. 9868, pp. 300\u2013309. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44636-3_28"},{"key":"21_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"708","DOI":"10.1007\/978-3-319-59153-7_61","volume-title":"Advances in Computational Intelligence","author":"M Dorado-Moreno","year":"2017","unstructured":"Dorado-Moreno, M., Cornejo-Bueno, L., Guti\u00e9rrez, P.A., Prieto, L., Salcedo-Sanz, S., Herv\u00e1s-Mart\u00ednez, C.: Combining reservoir computing and over-sampling for ordinal wind power ramp prediction. In: Rojas, I., Joya, G., Catala, A. (eds.) IWANN 2017. LNCS, vol. 10305, pp. 708\u2013719. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59153-7_61"},{"key":"21_CR6","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.renene.2017.04.016","volume":"111","author":"M Dorado-Moreno","year":"2017","unstructured":"Dorado-Moreno, M., Cornejo-Bueno, L., Guti\u00e9rrez, P.A., Prieto, L., Herv\u00e1s-Mart\u00ednez, C., Salcedo-Sanz, S.: Robust estimation of wind power ramp events with reservoir computing. Renew. Energy 111, 428\u2013437 (2017)","journal-title":"Renew. Energy"},{"key":"21_CR7","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1002\/qj.828","volume":"137","author":"DP Dee","year":"2011","unstructured":"Dee, D.P., Uppala, S.M., Simmons, A.J., Berrisford, P., Poli, P.: The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc. 137, 553\u2013597 (2011)","journal-title":"Q. J. R. Meteorol. Soc."},{"key":"21_CR8","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1016\/j.engappai.2015.03.012","volume":"43","author":"JC Fernandez","year":"2015","unstructured":"Fernandez, J.C., Salcedo-Sanz, S., Guti\u00e9rrez, P.A., Alexandre, E., Herv\u00e1s-Mart\u00ednez, C.: Significant wave height and energy flux range forecast with machine learning classifiers. Eng. Appl. Artif. Intell. 43, 44\u201353 (2015)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"21_CR9","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1109\/TKDE.2015.2457911","volume":"28","author":"PA Guti\u00e9rrez","year":"2016","unstructured":"Guti\u00e9rrez, P.A., P\u00e9rez-Ortiz, M., S\u00e1nchez-Monedero, J., Fern\u00e1ndez-Navarro, F., Herv\u00e1s-Mart\u00ednez, C.: Ordinal regression methods: survey and experimental study. IEEE Trans. Knowl. Data Eng. 28, 127\u2013146 (2016)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"21_CR10","unstructured":"Jaeger, H.: The \u2018echo state\u2019 approach to analysing and training recurrent neural networks. GMD report 148, German National Research Center for Information Technology, pp. 1\u201343 (2001)"},{"issue":"3","key":"21_CR11","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","volume":"3","author":"M Lukosevicius","year":"2009","unstructured":"Lukosevicius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Comput. Sci. Rev. 3(3), 127\u2013149 (2009)","journal-title":"Comput. Sci. Rev."},{"issue":"2","key":"21_CR12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1111\/j.2517-6161.1980.tb01109.x","volume":"42","author":"P McCullagh","year":"1980","unstructured":"McCullagh, P.: Regression models for ordinal data. J. R. Stat. Soc. 42(2), 109\u2013142 (1980)","journal-title":"J. R. Stat. Soc."},{"issue":"1","key":"21_CR13","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1109\/TNN.2010.2089641","volume":"22","author":"A Rodan","year":"2011","unstructured":"Rodan, A., Ti\u0148o, P.: Minimum complexity echo state network. IEEE Trans. Neural Netw. 22(1), 131\u2013144 (2011)","journal-title":"IEEE Trans. Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-03496-2_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T05:34:33Z","timestamp":1720762473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-03496-2_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030034955","9783030034962"],"references-count":13,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-03496-2_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"9 November 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Madrid","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":"21 November 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aida.ii.uam.es\/ideal2018\/#!\/main","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}