{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,10]],"date-time":"2024-09-10T20:28:34Z","timestamp":1726000114801},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030010683"},{"type":"electronic","value":"9783030010690"}],"license":[{"start":{"date-parts":[[2018,11,20]],"date-time":"2018-11-20T00:00:00Z","timestamp":1542672000000},"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":[[2019]]},"DOI":"10.1007\/978-3-030-01069-0_29","type":"book-chapter","created":{"date-parts":[[2018,11,19]],"date-time":"2018-11-19T15:45:36Z","timestamp":1542642336000},"page":"405-420","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Probabilistic Energy Forecasting Based on Self-organizing Inductive Modeling"],"prefix":"10.1007","author":[{"given":"Frank","family":"Lemke","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,11,20]]},"reference":[{"key":"29_CR1","unstructured":"European Union: A Framework Strategy for a Resilient Energy Union with a Forward-Looking Climate Change Policy (2015). http:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=COM:2015:80:FIN"},{"key":"29_CR2","unstructured":"Farlow, S.J., (ed.) Self-organizing Methods in Modeling. GMDH Type Algorithm. Marcel Dekker, New York (1984). ISBN 0-8247-7161-3"},{"key":"29_CR3","doi-asserted-by":"publisher","first-page":"896","DOI":"10.1016\/j.ijforecast.2016.02.001","volume":"32","author":"T Hong","year":"2016","unstructured":"Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., Hyndman, R.J.: Probabilistic energy forecasting: global\u00a0energy\u00a0forecasting\u00a0competition 2014 and beyond. Int. J. Forecast. 32, 896\u2013913 (2016)","journal-title":"Int. J. Forecast."},{"key":"29_CR4","first-page":"58","volume":"3","author":"AG Ivakhnenko","year":"1968","unstructured":"Ivakhnenko, A.G.: Group method of data handling as a rival of stochastic approximation method. Sov. Autom. Control. 3, 58\u201372 (1968)","journal-title":"Sov. Autom. Control."},{"key":"29_CR5","unstructured":"Ivakhnenko, A.G., Stepashko, V.S.: Pomechoustojcivost\u2019 modelirovanija (Noise-immunity of modeling). Naukova dumka, Kiev (1985). (In Russian)"},{"key":"29_CR6","unstructured":"KnowledgeMiner Software: INSIGHTS - Self-organizing modeling and forecasting tool, v6.1.3 (2018 A). https:\/\/www.knowledgeminer.eu . Last Accessed 05 May 2018"},{"key":"29_CR7","unstructured":"KnowledgeMiner Software: OCKHAM \u2013 Global Sensitivity Analysis tool, v2.0.1 (2018 B). https:\/\/www.knowledgeminer.eu\/ockham . Last Accessed 02 May 2018"},{"key":"29_CR8","doi-asserted-by":"crossref","unstructured":"Kondo, T., Ueno, J.: Feedback GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of the lungs. In: Proceedings of II International Workshop on Inductive Modelling, Czech Technical University, Prague, pp. 63\u201370 (2007)","DOI":"10.5687\/sss.2007.46"},{"key":"29_CR9","unstructured":"Kordik, P.: Fully automated knowledge extraction using group of adaptive model evolution. Ph.D. thesis, Department of Computer Science and Computers, FEE, CTU in Prague (2006)"},{"key":"29_CR10","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.matcom.2016.04.005","volume":"128","author":"Romain S.C. Lambert","year":"2016","unstructured":"Lambert, R., Lemke, F., Kucherenko, S., Song, S., Shah, N.: Global sensitivity analysis using sparse high dimensional model representations generated by the group method of data handling technique. J. Math. Comput. Simul. 128, 42\u201354 (2016)","journal-title":"Mathematics and Computers in Simulation"},{"key":"29_CR11","unstructured":"Madala, H.R., Ivakhnenko, A.G.: Inductive Learning Algorithms for Complex Systems Modelling. CRC Press Inc., Boca Raton, Ann Arbor, London, Tokyo (1994). ISBN 0-8493-4438-7"},{"key":"29_CR12","unstructured":"M\u00fcller, J.-A., Lemke, F.: Self-organizing Data Mining. Libri, Hamburg (2000). ISBN: 3-89811-861-4"},{"key":"29_CR13","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85\u2013117 (2015)","journal-title":"Neural Netw."},{"issue":"3","key":"29_CR14","first-page":"15","volume":"16","author":"VS Stepashko","year":"1983","unstructured":"Stepashko, V.S.: Potential noise immunity of modelling using a combinatorial GMDH algorithm without information regarding the noise. Sov. Autom. Control. 16(3), 15\u201325 (1983)","journal-title":"Sov. Autom. Control."},{"issue":"3","key":"29_CR15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1615\/JAutomatInfScien.v40.i3.20","volume":"40","author":"VS Stepashko","year":"2008","unstructured":"Stepashko, V.S.: Method of critical variances as analytical tool of theory of inductive modeling. J. Autom. Inf. Sci. 40(3), 4\u201322 (2008)","journal-title":"J. Autom. Inf. Sci."},{"key":"29_CR16","doi-asserted-by":"publisher","first-page":"1030","DOI":"10.1016\/j.ijforecast.2014.08.008","volume":"30","author":"R Weron","year":"2014","unstructured":"Weron, R.: Electricity price forecasting: a review of the state-of-the-art with a look into the future. Int. J. Forecast. 30, 1030\u20131081 (2014)","journal-title":"Int. J. Forecast."}],"container-title":["Advances in Intelligent Systems and Computing","Advances in Intelligent Systems and Computing III"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01069-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,6]],"date-time":"2022-09-06T12:18:37Z","timestamp":1662466717000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01069-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,20]]},"ISBN":["9783030010683","9783030010690"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01069-0_29","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2018,11,20]]},"assertion":[{"value":"CSIT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Conference on Computer Science and Information Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ukraine","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":"11 September 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"csit2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/csit.lp.edu.ua\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}