{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T01:51:23Z","timestamp":1743126683537,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030578015"},{"type":"electronic","value":"9783030578022"}],"license":[{"start":{"date-parts":[[2020,8,29]],"date-time":"2020-08-29T00:00:00Z","timestamp":1598659200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,29]],"date-time":"2020-08-29T00:00:00Z","timestamp":1598659200000},"content-version":"vor","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":[[2021]]},"DOI":"10.1007\/978-3-030-57802-2_65","type":"book-chapter","created":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T07:05:27Z","timestamp":1598598327000},"page":"681-690","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Copper Price Time Series Forecasting by Means of Generalized Regression Neural Networks with Optimized Predictor Variables"],"prefix":"10.1007","author":[{"given":"Gregorio Fidalgo","family":"Valverde","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alicja","family":"Krzemie\u0144","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro Riesgo","family":"Fern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco Javier Iglesias","family":"Rodr\u00edguez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana Su\u00e1rez","family":"S\u00e1nchez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,29]]},"reference":[{"key":"65_CR1","doi-asserted-by":"crossref","unstructured":"Matyjaszek, M., Fidalgo Valverde, G., Krzemie\u0144, A., Wodarski, K., Riesgo Fern\u00e1ndez, P.: Optimizing predictor variables in artificial neural networks when forecasting raw material prices for energy production. Energies 13, 15 (2020)","DOI":"10.3390\/en13082017"},{"issue":"1","key":"65_CR2","first-page":"3","volume":"64","author":"A Krzemie\u0144","year":"2019","unstructured":"Krzemie\u0144, A.: Dinamic fire risk prevention strategy in underground coal gasification processes by means of artificial neural networks. Arch. Min. Sci. 64(1), 3\u201319 (2019)","journal-title":"Arch. Min. Sci."},{"key":"65_CR3","unstructured":"Barab\u00e1si, A-L.: Network Science. 1st ed., Cambridge University Press, Cambridge (2016)"},{"key":"65_CR4","unstructured":"World Bank. \nhttp:\/\/pubdocs.worldbank.org\/en\/561011486076393416\/CMO-Historical-Data-Monthly.xlsx\n\n. Accessed 17 Apr 2020"},{"key":"65_CR5","unstructured":"Creative Commons Homepage (2008). \nhttps:\/\/creativecommons.org\/licenses\/by\/4.0\/\n\n. Accessed Jan 2020"},{"key":"65_CR6","unstructured":"Morantz, B.H., Whalen, T., Zhang, G.P.: A weighted window approach to neural network time series forecasting. In: Zhang, G.P. (ed.) Neural Networks in Business Forecasting. IRM Press (2004)"},{"key":"65_CR7","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.1016\/j.ins.2015.11.039","volume":"367","author":"Y Ren","year":"2016","unstructured":"Ren, Y., Suganthan, P.N., Srikanth, N., Amaratunga, G.: Random vector functional link network for short-term electricity load demand forecasting. Inf. Sci. 367, 1078\u20131093 (2016)","journal-title":"Inf. Sci."},{"key":"65_CR8","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1016\/j.resourpol.2019.02.017","volume":"61","author":"M Matyjaszek","year":"2019","unstructured":"Matyjaszek, M., Riesgo Fern\u00e1ndez, P., Krzemie\u0144, A., Wodarski, K., Fidalgo Valverde, G.: Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory. Resour. Policy 61, 283\u2013292 (2019)","journal-title":"Resour. Policy"},{"key":"65_CR9","unstructured":"Turmon, M.J., Fine, T.L.: Sample size requirements for feedforward neural networks. In: Advances in Neural Information Processing Systems, Denver, Colorado, USA, vol. 7, pp. 1\u201318 (1994)"},{"issue":"1","key":"65_CR10","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s11269-017-1807-2","volume":"32","author":"F Modaresi","year":"2017","unstructured":"Modaresi, F., Araghinejad, S., Ebrahimi, K.: A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and K-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resour. Manag. 32(1), 243\u2013258 (2017). \nhttps:\/\/doi.org\/10.1007\/s11269-017-1807-2","journal-title":"Water Resour. Manag."},{"key":"65_CR11","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.5194\/gmd-7-1247-2014","volume":"7","author":"T Chai","year":"2014","unstructured":"Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?-arguments against avoiding RMSE in the literature. Geosci. Model Dev. 7, 1247\u20131250 (2014)","journal-title":"Geosci. Model Dev."},{"key":"65_CR12","unstructured":"Lazaridis, A.G.: Prosody modelling using machine learning techniques for neutral and emotional speech synthesis, Department of Electrical and Computer Engineering Wire Communications Laboratory, University of Patras, Greece (2011)"},{"key":"65_CR13","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1016\/j.jsm.2016.04.002","volume":"14","author":"A Krzemie\u0144","year":"2015","unstructured":"Krzemie\u0144, A., Riesgo Fern\u00e1ndez, P., Su\u00e1rez S\u00e1nchez, A., S\u00e1nchez Lasheras, F.: Forecasting European thermal coal spot prices. J. Sustain. Min. 14, 203\u2013210 (2015)","journal-title":"J. Sustain. Min."},{"key":"65_CR14","doi-asserted-by":"crossref","unstructured":"Garc\u00eda Nieto, P.J., Alonso Fern\u00e1ndez, J.R.R., S\u00e1nchez Lasheras, F., de Cos Juez, F.J., D\u00edaz Mu\u00f1iz, C.: A new improved study of cyanotoxins presence from experimental cyanobacteria concentrations in the Trasona reservoir (Northern Spain) using the MARS technique. Scien. Tot. Environ. 430, 88\u201392 (2012)","DOI":"10.1016\/j.scitotenv.2012.04.068"},{"key":"65_CR15","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1016\/j.energy.2018.12.179","volume":"170","author":"A Krzemie\u0144","year":"2019","unstructured":"Krzemie\u0144, A.: Fire risk prevention in underground coal gasification (UCG) within active mines: temperature forecast by means of MARS models. Energy 170, 777\u2013790 (2019)","journal-title":"Energy"},{"key":"65_CR16","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.cam.2018.07.008","volume":"346","author":"C Ord\u00f3\u00f1ez","year":"2018","unstructured":"Ord\u00f3\u00f1ez, C., S\u00e1nchez Lasheras, F., Roca-Pardi\u00f1as, J., de Cos Juez, F.J.: A hybrid ARIMA\u2013SVM model for the study of the remaining useful life of aircraft engines. J. Comput. Appl. Math. 346, 184\u2013191 (2018)","journal-title":"J. Comput. Appl. Math."}],"container-title":["Advances in Intelligent Systems and Computing","15th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2020)"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-57802-2_65","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,28]],"date-time":"2020-08-28T07:20:51Z","timestamp":1598599251000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-57802-2_65"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,29]]},"ISBN":["9783030578015","9783030578022"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-57802-2_65","relation":{},"ISSN":["2194-5357","2194-5365"],"issn-type":[{"type":"print","value":"2194-5357"},{"type":"electronic","value":"2194-5365"}],"subject":[],"published":{"date-parts":[[2020,8,29]]},"assertion":[{"value":"29 August 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SOCO","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Soft Computing Models in Industrial and Environmental Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Burgos","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"socomoin2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/2020.sococonference.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}