{"id":56,"date":"2018-12-03T12:21:41","date_gmt":"2018-12-03T12:21:41","guid":{"rendered":"http:\/\/grupo.us.es\/minerva\/?page_id=56"},"modified":"2022-04-07T15:57:09","modified_gmt":"2022-04-07T15:57:09","slug":"publicaciones","status":"publish","type":"page","link":"https:\/\/grupo.us.es\/minerva\/publicaciones\/","title":{"rendered":"Publicaciones"},"content":{"rendered":"<div class=\"teachpress_pub_list\"><form name=\"tppublistform\" method=\"get\"><a name=\"tppubs\" id=\"tppubs\"><\/a><\/form><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">257 registros<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 de 13 <a href=\"https:\/\/grupo.us.es\/minerva\/publicaciones\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"p\u00e1gina siguiente\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/grupo.us.es\/minerva\/publicaciones\/?limit=13&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"\u00faltima p\u00e1gina\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><div class=\"teachpress_publication_list\"><h3 class=\"tp_h3\" id=\"tp_h3_2024\">2024<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Jim\u00e9nez-Navarro, Manuel J.;  Lovri\u0107, Mario;  Kecorius, Simonas;  Nyarko, Emmanuel Karlo;  Mart\u00ednez-Ballesteros, Mar\u00eda<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1561','tp_links')\" style=\"cursor:pointer;\">Explainable deep learning on multi-target time series forecasting: An air pollution use case<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Results in Engineering, <\/span><span class=\"tp_pub_additional_volume\">vol. 24, <\/span><span class=\"tp_pub_additional_pages\">pp. 103290, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1561\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1561','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1561\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1561','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1561\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1016\/j.rineng.2024.103290,<br \/>\r\ntitle = {Explainable deep learning on multi-target time series forecasting: An air pollution use case},<br \/>\r\nauthor = {Manuel J. Jim\u00e9nez-Navarro and Mario Lovri\u0107 and Simonas Kecorius and Emmanuel Karlo Nyarko and Mar\u00eda Mart\u00ednez-Ballesteros},<br \/>\r\nurl = {https:\/\/doi.org\/10.1016\/j.rineng.2024.103290},<br \/>\r\ndoi = {10.1016\/j.rineng.2024.103290},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Results in Engineering},<br \/>\r\nvolume = {24},<br \/>\r\npages = {103290},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1561','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1561\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1016\/j.rineng.2024.103290\" title=\"https:\/\/doi.org\/10.1016\/j.rineng.2024.103290\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.rineng.2024.103290<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.rineng.2024.103290\" title=\"DOI de seguimiento:10.1016\/j.rineng.2024.103290\" target=\"_blank\">doi:10.1016\/j.rineng.2024.103290<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1561','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Jim\u00e9nez-Navarro, M. J.;  Mart\u00ednez-Ballesteros, M.;  Mart\u00ednez-\u00c1lvarez, F.;  Asencio-Cort\u00e9s, G.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1562','tp_links')\" style=\"cursor:pointer;\">Explaining deep learning models for ozone pollution prediction via embedded feature selection<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Applied Soft Computing, <\/span><span class=\"tp_pub_additional_volume\">vol. 157, <\/span><span class=\"tp_pub_additional_pages\">pp. 111504, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1562\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1562','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1562\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1562','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1562\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1016\/j.asoc.2024.111504,<br \/>\r\ntitle = {Explaining deep learning models for ozone pollution prediction via embedded feature selection},<br \/>\r\nauthor = {M. J. Jim\u00e9nez-Navarro and M. Mart\u00ednez-Ballesteros and F. Mart\u00ednez-\u00c1lvarez and G. Asencio-Cort\u00e9s},<br \/>\r\nurl = {https:\/\/doi.org\/10.1016\/j.asoc.2024.111504},<br \/>\r\ndoi = {10.1016\/j.asoc.2024.111504},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Applied Soft Computing},<br \/>\r\nvolume = {157},<br \/>\r\npages = {111504},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1562','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1562\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1016\/j.asoc.2024.111504\" title=\"https:\/\/doi.org\/10.1016\/j.asoc.2024.111504\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.asoc.2024.111504<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.asoc.2024.111504\" title=\"DOI de seguimiento:10.1016\/j.asoc.2024.111504\" target=\"_blank\">doi:10.1016\/j.asoc.2024.111504<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1562','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Linares-Barrera, Maria Lourdes;  Jimenez-Navarro, Manuel J.;  Brito, Isabel Sofia;  Riquelme, Jos\u00e9 C.;  Mart\u00ednez-Ballesteros, Mar\u00eda<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1563','tp_links')\" style=\"cursor:pointer;\">Evolutionary Feature Selection for Time-Series Forecasting<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. 395-399, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1563\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1563','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1563\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1563','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1563\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1145\/3605098.3636191,<br \/>\r\ntitle = {Evolutionary Feature Selection for Time-Series Forecasting},<br \/>\r\nauthor = {Maria Lourdes Linares-Barrera and Manuel J. Jimenez-Navarro and Isabel Sofia Brito and Jos\u00e9 C. Riquelme and Mar\u00eda Mart\u00ednez-Ballesteros},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3605098.3636191},<br \/>\r\ndoi = {10.1145\/3605098.3636191},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {395-399},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1563','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1563\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3605098.3636191\" title=\"https:\/\/doi.org\/10.1145\/3605098.3636191\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3605098.3636191<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3605098.3636191\" title=\"DOI de seguimiento:10.1145\/3605098.3636191\" target=\"_blank\">doi:10.1145\/3605098.3636191<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1563','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Troncoso-Garc\u00eda, Angela Robledo;  Jim\u00e9nez-Navarro, Manuel Jes\u00fas;  Mart\u00ednez-\u00c1lvarez, Francisco;  Troncoso, Alicia<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1564','tp_links')\" style=\"cursor:pointer;\">Ground-Level Ozone Forecasting Using Explainable Machine Learning<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Lecture Notes in Computer Science, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. 71-80, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1564\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1564','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1564\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1564','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1564\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1007\/978-3-031-62799-6_8,<br \/>\r\ntitle = {Ground-Level Ozone Forecasting Using Explainable Machine Learning},<br \/>\r\nauthor = {Angela Robledo Troncoso-Garc\u00eda and Manuel Jes\u00fas Jim\u00e9nez-Navarro and Francisco Mart\u00ednez-\u00c1lvarez and Alicia Troncoso},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-3-031-62799-6_8},<br \/>\r\ndoi = {10.1007\/978-3-031-62799-6_8},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Lecture Notes in Computer Science},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {71-80},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1564','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1564\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_8\" title=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_8\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-62799-6_8<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-62799-6_8\" title=\"DOI de seguimiento:10.1007\/978-3-031-62799-6_8\" target=\"_blank\">doi:10.1007\/978-3-031-62799-6_8<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1564','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Linares-Barrera, Mar\u00eda Lourdes;  Navarro, Manuel J. Jim\u00e9nez;  Riquelme, Jos\u00e9 C.;  Mart\u00ednez-Ballesteros, Mar\u00eda<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1565','tp_links')\" style=\"cursor:pointer;\">Multi-Objective Lagged Feature Selection Based on\u00a0Dependence Coefficient for\u00a0Time-Series Forecasting<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Lecture Notes in Computer Science, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. 81-90, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1565\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1565','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1565\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1565','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1565\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1007\/978-3-031-62799-6_9,<br \/>\r\ntitle = {Multi-Objective Lagged Feature Selection Based on\u00a0Dependence Coefficient for\u00a0Time-Series Forecasting},<br \/>\r\nauthor = {Mar\u00eda Lourdes Linares-Barrera and Manuel J. Jim\u00e9nez Navarro and Jos\u00e9 C. Riquelme and Mar\u00eda Mart\u00ednez-Ballesteros},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-3-031-62799-6_9},<br \/>\r\ndoi = {10.1007\/978-3-031-62799-6_9},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Lecture Notes in Computer Science},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {81-90},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1565','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1565\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_9\" title=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_9\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-62799-6_9<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-62799-6_9\" title=\"DOI de seguimiento:10.1007\/978-3-031-62799-6_9\" target=\"_blank\">doi:10.1007\/978-3-031-62799-6_9<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1565','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Gal\u00e1n-Sales, Francisco Javier;  Linares-Barrera, Mar\u00eda Lourdes;  Reina-Jim\u00e9nez, Pablo;  Rodr\u00edguez-L\u00f3pez, Ana;  Jim\u00e9nez-Navarro, Manuel Jes\u00fas<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1566','tp_links')\" style=\"cursor:pointer;\">Toward Explaining Competitive Success in\u00a0League of\u00a0Legends: A Machine Learning Analysis<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Lecture Notes in Computer Science, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. 184-193, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1566\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1566','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1566\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1566','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1566\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1007\/978-3-031-62799-6_19,<br \/>\r\ntitle = {Toward Explaining Competitive Success in\u00a0League of\u00a0Legends: A Machine Learning Analysis},<br \/>\r\nauthor = {Francisco Javier Gal\u00e1n-Sales and Mar\u00eda Lourdes Linares-Barrera and Pablo Reina-Jim\u00e9nez and Ana Rodr\u00edguez-L\u00f3pez and Manuel Jes\u00fas Jim\u00e9nez-Navarro},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-3-031-62799-6_19},<br \/>\r\ndoi = {10.1007\/978-3-031-62799-6_19},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Lecture Notes in Computer Science},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {184-193},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1566','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1566\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_19\" title=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_19\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-62799-6_19<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-62799-6_19\" title=\"DOI de seguimiento:10.1007\/978-3-031-62799-6_19\" target=\"_blank\">doi:10.1007\/978-3-031-62799-6_19<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1566','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Campos-Romero, Miguel;  Carranza-Garc\u00eda, Manuel;  Riquelme, Jos\u00e9 C.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1578','tp_links')\" style=\"cursor:pointer;\">Advancing unsupervised anomaly detection with normalizing flow and multi-scale ensemble learning<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Engineering Applications of Artificial Intelligence, <\/span><span class=\"tp_pub_additional_volume\">vol. 137, <\/span><span class=\"tp_pub_additional_pages\">pp. 109088, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1578\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1578','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1578\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1578','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1578\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1016\/j.engappai.2024.109088,<br \/>\r\ntitle = {Advancing unsupervised anomaly detection with normalizing flow and multi-scale ensemble learning},<br \/>\r\nauthor = {Miguel Campos-Romero and Manuel Carranza-Garc\u00eda and Jos\u00e9 C. Riquelme},<br \/>\r\nurl = {https:\/\/doi.org\/10.1016\/j.engappai.2024.109088},<br \/>\r\ndoi = {10.1016\/j.engappai.2024.109088},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Engineering Applications of Artificial Intelligence},<br \/>\r\nvolume = {137},<br \/>\r\npages = {109088},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1578','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1578\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1016\/j.engappai.2024.109088\" title=\"https:\/\/doi.org\/10.1016\/j.engappai.2024.109088\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.engappai.2024.109088<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.engappai.2024.109088\" title=\"DOI de seguimiento:10.1016\/j.engappai.2024.109088\" target=\"_blank\">doi:10.1016\/j.engappai.2024.109088<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1578','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Riquelme-Dominguez, Jose Miguel;  Carranza-Garc\u00eda, Manuel;  Lara-Ben\u00edtez, Pedro;  Gonz\u00e1lez-Longatt, Francisco M.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1579','tp_links')\" style=\"cursor:pointer;\">A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">International Journal of Electrical Power &amp;amp; Energy Systems, <\/span><span class=\"tp_pub_additional_volume\">vol. 156, <\/span><span class=\"tp_pub_additional_pages\">pp. 109730, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1579\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1579','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1579\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1579','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1579\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1016\/j.ijepes.2023.109730,<br \/>\r\ntitle = {A machine learning-based methodology for short-term kinetic energy forecasting with real-time application: Nordic Power System case},<br \/>\r\nauthor = {Jose Miguel Riquelme-Dominguez and Manuel Carranza-Garc\u00eda and Pedro Lara-Ben\u00edtez and Francisco M. Gonz\u00e1lez-Longatt},<br \/>\r\nurl = {https:\/\/doi.org\/10.1016\/j.ijepes.2023.109730},<br \/>\r\ndoi = {10.1016\/j.ijepes.2023.109730},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {International Journal of Electrical Power &amp; Energy Systems},<br \/>\r\nvolume = {156},<br \/>\r\npages = {109730},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1579','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1579\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1016\/j.ijepes.2023.109730\" title=\"https:\/\/doi.org\/10.1016\/j.ijepes.2023.109730\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.ijepes.2023.109730<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.ijepes.2023.109730\" title=\"DOI de seguimiento:10.1016\/j.ijepes.2023.109730\" target=\"_blank\">doi:10.1016\/j.ijepes.2023.109730<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1579','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Luna-Romera, Jos\u00e9 Mar\u00eda;  Carranza-Garc\u00eda, Manuel;  Arcos-Vargas, \u00c1ngel;  Riquelme-Santos, Jos\u00e9 C.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1580','tp_links')\" style=\"cursor:pointer;\">An empirical analysis of the relationship among price, demand and CO2 emissions in the Spanish electricity market<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Heliyon, <\/span><span class=\"tp_pub_additional_volume\">vol. 10, <\/span><span class=\"tp_pub_additional_pages\">pp. e25838, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1580\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1580','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1580\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1580','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1580\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1016\/j.heliyon.2024.e25838,<br \/>\r\ntitle = {An empirical analysis of the relationship among price, demand and CO2 emissions in the Spanish electricity market},<br \/>\r\nauthor = {Jos\u00e9 Mar\u00eda Luna-Romera and Manuel Carranza-Garc\u00eda and \u00c1ngel Arcos-Vargas and Jos\u00e9 C. Riquelme-Santos},<br \/>\r\nurl = {https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25838},<br \/>\r\ndoi = {10.1016\/j.heliyon.2024.e25838},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Heliyon},<br \/>\r\nvolume = {10},<br \/>\r\npages = {e25838},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1580','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1580\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25838\" title=\"https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25838\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25838<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.heliyon.2024.e25838\" title=\"DOI de seguimiento:10.1016\/j.heliyon.2024.e25838\" target=\"_blank\">doi:10.1016\/j.heliyon.2024.e25838<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1580','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Salazar-Gonz\u00e1lez, Jos\u00e9 L.;  Luna-Romera, Jos\u00e9 Mar\u00eda;  Carranza-Garc\u00eda, Manuel;  \u00c1lvarez-Garc\u00eda, Juan A.;  Soria-Morillo, Luis M.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1581','tp_links')\" style=\"cursor:pointer;\">Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Integrated Computer-Aided Engineering, <\/span><span class=\"tp_pub_additional_volume\">vol. 31, <\/span><span class=\"tp_pub_additional_pages\">pp. 307-326, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1581\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1581','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1581\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1581','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1581\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.3233\/ica-230726,<br \/>\r\ntitle = {Enhancing smart home appliance recognition with wavelet and scalogram analysis using data augmentation},<br \/>\r\nauthor = {Jos\u00e9 L. Salazar-Gonz\u00e1lez and Jos\u00e9 Mar\u00eda Luna-Romera and Manuel Carranza-Garc\u00eda and Juan A. \u00c1lvarez-Garc\u00eda and Luis M. Soria-Morillo},<br \/>\r\nurl = {https:\/\/doi.org\/10.3233\/ica-230726},<br \/>\r\ndoi = {10.3233\/ica-230726},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Integrated Computer-Aided Engineering},<br \/>\r\nvolume = {31},<br \/>\r\npages = {307-326},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1581','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1581\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.3233\/ica-230726\" title=\"https:\/\/doi.org\/10.3233\/ica-230726\" target=\"_blank\">https:\/\/doi.org\/10.3233\/ica-230726<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3233\/ica-230726\" title=\"DOI de seguimiento:10.3233\/ica-230726\" target=\"_blank\">doi:10.3233\/ica-230726<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1581','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Jim\u00e9nez-Navarro, M. J.;  Troncoso-Garc\u00eda, A. R.;  Troncoso, A.;  Mart\u00ednez-\u00c1lvarez, F.;  Mart\u00ednez-Ballesteros, M.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1594','tp_links')\" style=\"cursor:pointer;\">Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">2024 International Conference on Smart Systems and Technologies (SST), <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. 153-158, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1594\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1594','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1594\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1594','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1594\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1109\/sst61991.2024.10755283,<br \/>\r\ntitle = {Explainable Deep Learning with Embedded Feature Selection for Electricity Demand Forecasting},<br \/>\r\nauthor = {M. J. Jim\u00e9nez-Navarro and A. R. Troncoso-Garc\u00eda and A. Troncoso and F. Mart\u00ednez-\u00c1lvarez and M. Mart\u00ednez-Ballesteros},<br \/>\r\nurl = {https:\/\/doi.org\/10.1109\/sst61991.2024.10755283},<br \/>\r\ndoi = {10.1109\/sst61991.2024.10755283},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {2024 International Conference on Smart Systems and Technologies (SST)},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {153-158},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1594','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1594\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1109\/sst61991.2024.10755283\" title=\"https:\/\/doi.org\/10.1109\/sst61991.2024.10755283\" target=\"_blank\">https:\/\/doi.org\/10.1109\/sst61991.2024.10755283<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1109\/sst61991.2024.10755283\" title=\"DOI de seguimiento:10.1109\/sst61991.2024.10755283\" target=\"_blank\">doi:10.1109\/sst61991.2024.10755283<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1594','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Jim\u00c9nez-Navarro, M J;  Mart\u00cdnez-Ballesteros, M;  Brito, I S;  Mart\u00cdnez-\u00c1lvarez, F;  Asencio-Cort\u00c9s, G<\/p><p class=\"tp_pub_title\"><a href=\"https:\/\/grupo.us.es\/minerva\/embedded-feature-selection-for-neural-networks-via-learnable-drop-layer\/\">Embedded feature selection for neural networks via learnable drop layer<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Logic Journal of the IGPL, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. N\/A, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1595\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1595','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1595\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1595','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1595\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1093\/jigpal\/jzae062,<br \/>\r\ntitle = {Embedded feature selection for neural networks via learnable drop layer},<br \/>\r\nauthor = {M J Jim\u00c9nez-Navarro and M Mart\u00cdnez-Ballesteros and I S Brito and F Mart\u00cdnez-\u00c1lvarez and G Asencio-Cort\u00c9s},<br \/>\r\nurl = {https:\/\/doi.org\/10.1093\/jigpal\/jzae062},<br \/>\r\ndoi = {10.1093\/jigpal\/jzae062},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Logic Journal of the IGPL},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {N\/A},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1595','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1595\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1093\/jigpal\/jzae062\" title=\"https:\/\/doi.org\/10.1093\/jigpal\/jzae062\" target=\"_blank\">https:\/\/doi.org\/10.1093\/jigpal\/jzae062<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1093\/jigpal\/jzae062\" title=\"DOI de seguimiento:10.1093\/jigpal\/jzae062\" target=\"_blank\">doi:10.1093\/jigpal\/jzae062<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1595','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Jim\u00e9nez-Navarro, M J;  Mart\u00ednez-Ballesteros, M;  Mart\u00ednez-\u00c1lvarez, F;  Troncoso, A;  Asencio-Cort\u00e9s, G<\/p><p class=\"tp_pub_title\"><a href=\"https:\/\/grupo.us.es\/minerva\/from-simple-to-complex-a-sequential-method-for-enhancing-time-series-forecasting-with-deep-learning\/\">From simple to complex: a sequential method for enhancing time series forecasting with deep learning<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Logic Journal of the IGPL, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. N\/A, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1596\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1596','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1596\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1596','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1596\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1093\/jigpal\/jzae030,<br \/>\r\ntitle = {From simple to complex: a sequential method for enhancing time series forecasting with deep learning},<br \/>\r\nauthor = {M J Jim\u00e9nez-Navarro and M Mart\u00ednez-Ballesteros and F Mart\u00ednez-\u00c1lvarez and A Troncoso and G Asencio-Cort\u00e9s},<br \/>\r\nurl = {https:\/\/doi.org\/10.1093\/jigpal\/jzae030},<br \/>\r\ndoi = {10.1093\/jigpal\/jzae030},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Logic Journal of the IGPL},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {N\/A},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1596','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1596\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1093\/jigpal\/jzae030\" title=\"https:\/\/doi.org\/10.1093\/jigpal\/jzae030\" target=\"_blank\">https:\/\/doi.org\/10.1093\/jigpal\/jzae030<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1093\/jigpal\/jzae030\" title=\"DOI de seguimiento:10.1093\/jigpal\/jzae030\" target=\"_blank\">doi:10.1093\/jigpal\/jzae030<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1596','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Gutierrez-Aviles, David;  Torres, Jose F.;  Martinez-Alvarez, Francisco;  Cugliari, Jairo<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1607','tp_links')\" style=\"cursor:pointer;\">An evolutionary triclustering approach to discover electricity consumption patterns in France<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. 386-394, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1607\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1607','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1607\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1607','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1607\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1145\/3605098.3636034,<br \/>\r\ntitle = {An evolutionary triclustering approach to discover electricity consumption patterns in France},<br \/>\r\nauthor = {David Gutierrez-Aviles and Jose F. Torres and Francisco Martinez-Alvarez and Jairo Cugliari},<br \/>\r\nurl = {https:\/\/doi.org\/10.1145\/3605098.3636034},<br \/>\r\ndoi = {10.1145\/3605098.3636034},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {386-394},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1607','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1607\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1145\/3605098.3636034\" title=\"https:\/\/doi.org\/10.1145\/3605098.3636034\" target=\"_blank\">https:\/\/doi.org\/10.1145\/3605098.3636034<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1145\/3605098.3636034\" title=\"DOI de seguimiento:10.1145\/3605098.3636034\" target=\"_blank\">doi:10.1145\/3605098.3636034<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1607','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Rodr\u00edguez-D\u00edaz, Francesc;  Torres, Jos\u00e9 Francisco;  Guti\u00e9rrez-Avil\u00e9s, David;  Troncoso, Alicia;  Mart\u00ednez-\u00c1lvarez, Francisco<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1608','tp_links')\" style=\"cursor:pointer;\">An Experimental Comparison of\u00a0Qiskit and\u00a0Pennylane for\u00a0Hybrid Quantum-Classical Support Vector Machines<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Lecture Notes in Computer Science, <\/span><span class=\"tp_pub_additional_volume\">vol. N\/A, <\/span><span class=\"tp_pub_additional_pages\">pp. 121-130, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1608\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1608','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1608\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1608','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1608\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1007\/978-3-031-62799-6_13,<br \/>\r\ntitle = {An Experimental Comparison of\u00a0Qiskit and\u00a0Pennylane for\u00a0Hybrid Quantum-Classical Support Vector Machines},<br \/>\r\nauthor = {Francesc Rodr\u00edguez-D\u00edaz and Jos\u00e9 Francisco Torres and David Guti\u00e9rrez-Avil\u00e9s and Alicia Troncoso and Francisco Mart\u00ednez-\u00c1lvarez},<br \/>\r\nurl = {https:\/\/doi.org\/10.1007\/978-3-031-62799-6_13},<br \/>\r\ndoi = {10.1007\/978-3-031-62799-6_13},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Lecture Notes in Computer Science},<br \/>\r\nvolume = {N\/A},<br \/>\r\npages = {121-130},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1608','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1608\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_13\" title=\"https:\/\/doi.org\/10.1007\/978-3-031-62799-6_13\" target=\"_blank\">https:\/\/doi.org\/10.1007\/978-3-031-62799-6_13<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1007\/978-3-031-62799-6_13\" title=\"DOI de seguimiento:10.1007\/978-3-031-62799-6_13\" target=\"_blank\">doi:10.1007\/978-3-031-62799-6_13<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1608','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Sanchez-Lopez, Jose E.;  Sol\u00eds-Garc\u00eda, Javier;  Riquelme, Jose C.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1622','tp_links')\" style=\"cursor:pointer;\">Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">International Journal of Electrical Power &amp;amp; Energy Systems, <\/span><span class=\"tp_pub_additional_volume\">vol. 158, <\/span><span class=\"tp_pub_additional_pages\">pp. 109947, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1622\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1622','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1622\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1622','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1622\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1016\/j.ijepes.2024.109947,<br \/>\r\ntitle = {Semi-real-time decision tree ensemble algorithms for very short-term solar irradiance forecasting},<br \/>\r\nauthor = {Jose E. Sanchez-Lopez and Javier Sol\u00eds-Garc\u00eda and Jose C. Riquelme},<br \/>\r\nurl = {https:\/\/doi.org\/10.1016\/j.ijepes.2024.109947},<br \/>\r\ndoi = {10.1016\/j.ijepes.2024.109947},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {International Journal of Electrical Power &amp; Energy Systems},<br \/>\r\nvolume = {158},<br \/>\r\npages = {109947},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1622','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1622\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1016\/j.ijepes.2024.109947\" title=\"https:\/\/doi.org\/10.1016\/j.ijepes.2024.109947\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.ijepes.2024.109947<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.ijepes.2024.109947\" title=\"DOI de seguimiento:10.1016\/j.ijepes.2024.109947\" target=\"_blank\">doi:10.1016\/j.ijepes.2024.109947<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1622','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Aldarraji, Morteza;  Vega-M\u00e1rquez, Bel\u00e9n;  Pontes, Beatriz;  Mahmood, Basim;  Riquelme, Jos\u00e9 C.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1623','tp_links')\" style=\"cursor:pointer;\">Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Heliyon, <\/span><span class=\"tp_pub_additional_volume\">vol. 10, <\/span><span class=\"tp_pub_additional_pages\">pp. e25821, <\/span><span class=\"tp_pub_additional_year\">2024<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1623\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1623','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1623\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1623','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1623\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{10.1016\/j.heliyon.2024.e25821,<br \/>\r\ntitle = {Addressing energy challenges in Iraq: Forecasting power supply and demand using artificial intelligence models},<br \/>\r\nauthor = {Morteza Aldarraji and Bel\u00e9n Vega-M\u00e1rquez and Beatriz Pontes and Basim Mahmood and Jos\u00e9 C. Riquelme},<br \/>\r\nurl = {https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25821},<br \/>\r\ndoi = {10.1016\/j.heliyon.2024.e25821},<br \/>\r\nyear  = {2024},<br \/>\r\ndate = {2024-01-01},<br \/>\r\njournal = {Heliyon},<br \/>\r\nvolume = {10},<br \/>\r\npages = {e25821},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1623','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1623\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25821\" title=\"https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25821\" target=\"_blank\">https:\/\/doi.org\/10.1016\/j.heliyon.2024.e25821<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.1016\/j.heliyon.2024.e25821\" title=\"DOI de seguimiento:10.1016\/j.heliyon.2024.e25821\" target=\"_blank\">doi:10.1016\/j.heliyon.2024.e25821<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1623','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><h3 class=\"tp_h3\" id=\"tp_h3_2023\">2023<\/h3><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Troncoso-Garc\u00eda, A. R.;  Mart\u00ednez-Ballesteros, M.;  Mart\u00ednez-\u00c1lvarez, F.;  Troncoso, A.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1537','tp_links')\" style=\"cursor:pointer;\">A new approach based on association rules to add explainability to time series forecasting models<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Information Fusion, <\/span><span class=\"tp_pub_additional_volume\">vol. 94, <\/span><span class=\"tp_pub_additional_pages\">pp. 169-180, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 1566-2535<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_1537\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1537','tp_abstract')\" title=\"Mostrar resumen\" style=\"cursor:pointer;\">Resumen<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_1537\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1537','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1537\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1537','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1537\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{TRONCOSOGARCIA2023169,<br \/>\r\ntitle = {A new approach based on association rules to add explainability to time series forecasting models},<br \/>\r\nauthor = {A. R. Troncoso-Garc\u00eda and M. Mart\u00ednez-Ballesteros and F. Mart\u00ednez-\u00c1lvarez and A. Troncoso},<br \/>\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253523000295},<br \/>\r\ndoi = {https:\/\/doi.org\/10.1016\/j.inffus.2023.01.021},<br \/>\r\nissn = {1566-2535},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\njournal = {Information Fusion},<br \/>\r\nvolume = {94},<br \/>\r\npages = {169-180},<br \/>\r\nabstract = {Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1537','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_1537\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Machine learning and deep learning have become the most useful and powerful tools in the last years to mine information from large datasets. Despite the successful application to many research fields, it is widely known that some of these solutions based on artificial intelligence are considered black-box models, meaning that most experts find difficult to explain and interpret the models and why they generate such outputs. In this context, explainable artificial intelligence is emerging with the aim of providing black-box models with sufficient interpretability. Thus, models could be easily understood and further applied. This work proposes a novel method to explain black-box models, by using numeric association rules to explain and interpret multi-step time series forecasting models. Thus, a multi-objective algorithm is used to discover quantitative association rules from the target model. Then, visual explanation techniques are applied to make the rules more interpretable. Data from Spanish electricity energy consumption has been used to assess the suitability of the proposal.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1537','tp_abstract')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1537\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253523000295\" title=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253523000295\" target=\"_blank\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1566253523000295<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1016\/j.inffus.2023.01.021\" title=\"DOI de seguimiento:https:\/\/doi.org\/10.1016\/j.inffus.2023.01.021\" target=\"_blank\">doi:https:\/\/doi.org\/10.1016\/j.inffus.2023.01.021<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1537','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Melgar-Garc\u00eda, Laura;  Guti\u00e9rrez-Avil\u00e9s, David;  Rubio-Escudero, Cristina;  Troncoso, Alicia<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1547','tp_links')\" style=\"cursor:pointer;\">A Novel Distributed Forecasting Method Based on Information Fusion and Incremental Learning for Streaming Time Series<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">SSRN Electronic Journal, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_resource_link\"><a id=\"tp_links_sh_1547\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1547','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1547\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1547','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1547\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{articleh,<br \/>\r\ntitle = {A Novel Distributed Forecasting Method Based on Information Fusion and Incremental Learning for Streaming Time Series},<br \/>\r\nauthor = {Laura Melgar-Garc\u00eda and David Guti\u00e9rrez-Avil\u00e9s and Cristina Rubio-Escudero and Alicia Troncoso},<br \/>\r\ndoi = {10.2139\/ssrn.4326600},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\njournal = {SSRN Electronic Journal},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1547','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1547\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.2139\/ssrn.4326600\" title=\"DOI de seguimiento:10.2139\/ssrn.4326600\" target=\"_blank\">doi:10.2139\/ssrn.4326600<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1547','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><div class=\"tp_publication tp_publication_article\"><div class=\"tp_pub_info\"><p class=\"tp_pub_author\"> Jim\u00e9nez-Navarro, M. J.;  Mart\u00ednez-Ballesteros, M.;  Mart\u00ednez-\u00c1lvarez, F.;  Asencio-Cort\u00e9s, G.<\/p><p class=\"tp_pub_title\"><a class=\"tp_title_link\" onclick=\"teachpress_pub_showhide('1557','tp_links')\" style=\"cursor:pointer;\">PHILNet: A novel efficient approach for time series forecasting using deep learning<\/a> <span class=\"tp_pub_type tp_  article\">Art\u00edculo de revista<\/span> <\/p><p class=\"tp_pub_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Information Sciences, <\/span><span class=\"tp_pub_additional_volume\">vol. 632, <\/span><span class=\"tp_pub_additional_pages\">pp. 815-832, <\/span><span class=\"tp_pub_additional_year\">2023<\/span>, <span class=\"tp_pub_additional_issn\">ISSN: 0020-0255<\/span>.<\/p><p class=\"tp_pub_menu\"><span class=\"tp_abstract_link\"><a id=\"tp_abstract_sh_1557\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1557','tp_abstract')\" title=\"Mostrar resumen\" style=\"cursor:pointer;\">Resumen<\/a><\/span> | <span class=\"tp_resource_link\"><a id=\"tp_links_sh_1557\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1557','tp_links')\" title=\"Mostrar enlaces y recursos\" style=\"cursor:pointer;\">Enlaces<\/a><\/span> | <span class=\"tp_bibtex_link\"><a id=\"tp_bibtex_sh_1557\" class=\"tp_show\" onclick=\"teachpress_pub_showhide('1557','tp_bibtex')\" title=\"Mostrar entrada BibTeX \" style=\"cursor:pointer;\">BibTeX<\/a><\/span><\/p><div class=\"tp_bibtex\" id=\"tp_bibtex_1557\" style=\"display:none;\"><div class=\"tp_bibtex_entry\"><pre>@article{JIMENEZNAVARRO2023815,<br \/>\r\ntitle = {PHILNet: A novel efficient approach for time series forecasting using deep learning},<br \/>\r\nauthor = {M. J. Jim\u00e9nez-Navarro and M. Mart\u00ednez-Ballesteros and F. Mart\u00ednez-\u00c1lvarez and G. Asencio-Cort\u00e9s},<br \/>\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025523003183},<br \/>\r\ndoi = {https:\/\/doi.org\/10.1016\/j.ins.2023.03.021},<br \/>\r\nissn = {0020-0255},<br \/>\r\nyear  = {2023},<br \/>\r\ndate = {2023-01-01},<br \/>\r\njournal = {Information Sciences},<br \/>\r\nvolume = {632},<br \/>\r\npages = {815-832},<br \/>\r\nabstract = {Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series.},<br \/>\r\nkeywords = {},<br \/>\r\npubstate = {published},<br \/>\r\ntppubtype = {article}<br \/>\r\n}<br \/>\r\n<\/pre><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1557','tp_bibtex')\">Cerrar<\/a><\/p><\/div><div class=\"tp_abstract\" id=\"tp_abstract_1557\" style=\"display:none;\"><div class=\"tp_abstract_entry\">Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms have been presented in this area, where the input is processed through a series of non-linear functions to produce the output. We present a novel strategy to improve the performance of deep learning models in time series forecasting in terms of efficiency while reaching similar effectiveness. This approach separates the model into levels, starting with the easiest and continuing to the most difficult. The simpler levels deal with smoothed versions of the input, whereas the most sophisticated level deals with the raw data. This strategy seeks to mimic the human learning process, in which basic tasks are completed initially, followed by more precise and sophisticated ones. Our method achieved promising results, obtaining a 35% improvement in mean squared error and a 2.6 time decrease in training time compared with the best models found in a variety of time series.<\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1557','tp_abstract')\">Cerrar<\/a><\/p><\/div><div class=\"tp_links\" id=\"tp_links_1557\" style=\"display:none;\"><div class=\"tp_links_entry\"><ul class=\"tp_pub_list\"><li><i class=\"fas fa-globe\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025523003183\" title=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025523003183\" target=\"_blank\">https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0020025523003183<\/a><\/li><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1016\/j.ins.2023.03.021\" title=\"DOI de seguimiento:https:\/\/doi.org\/10.1016\/j.ins.2023.03.021\" target=\"_blank\">doi:https:\/\/doi.org\/10.1016\/j.ins.2023.03.021<\/a><\/li><\/ul><\/div><p class=\"tp_close_menu\"><a class=\"tp_close\" onclick=\"teachpress_pub_showhide('1557','tp_links')\">Cerrar<\/a><\/p><\/div><\/div><\/div><\/div><div class=\"tablenav\"><div class=\"tablenav-pages\"><span class=\"displaying-num\">257 registros<\/span> <a class=\"page-numbers button disabled\">&laquo;<\/a> <a class=\"page-numbers button disabled\">&lsaquo;<\/a> 1 de 13 <a href=\"https:\/\/grupo.us.es\/minerva\/publicaciones\/?limit=2&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"p\u00e1gina siguiente\" class=\"page-numbers button\">&rsaquo;<\/a> <a href=\"https:\/\/grupo.us.es\/minerva\/publicaciones\/?limit=13&amp;tgid=&amp;yr=&amp;type=&amp;usr=&amp;auth=&amp;tsr=#tppubs\" title=\"\u00faltima p\u00e1gina\" class=\"page-numbers button\">&raquo;<\/a> <\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/template_fullwidth.php","meta":{"footnotes":""},"class_list":["post-56","page","type-page","status-publish","hentry","post-item clearfix"],"_links":{"self":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/pages\/56","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/comments?post=56"}],"version-history":[{"count":0,"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/pages\/56\/revisions"}],"wp:attachment":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/media?parent=56"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}