{"id":473,"date":"2020-03-28T12:00:00","date_gmt":"2020-03-28T12:00:00","guid":{"rendered":"http:\/\/grupo.us.es\/minerva\/temporal-convolutional-networks-applied-to-energy-related-time-series-forecasting-12\/"},"modified":"2020-03-28T12:00:00","modified_gmt":"2020-03-28T12:00:00","slug":"temporal-convolutional-networks-applied-to-energy-related-time-series-forecasting-12","status":"publish","type":"page","link":"https:\/\/grupo.us.es\/minerva\/temporal-convolutional-networks-applied-to-energy-related-time-series-forecasting-12\/","title":{"rendered":"Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting"},"content":{"rendered":"<p><div class=\"tp_single_publication\"><span class=\"tp_single_author\">P Lara-Ben\u00edtez, M Carranza-Garc\u00eda, J M Luna-Romera, J C Riquelme: <\/span> <span class=\"tp_single_title\">Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting<\/span>. <span class=\"tp_single_additional\"><span class=\"tp_pub_additional_in\">En: <\/span><span class=\"tp_pub_additional_journal\">Applied Sciences, <\/span><span class=\"tp_pub_additional_volume\">vol. 10, <\/span><span class=\"tp_pub_additional_number\">no 7, <\/span><span class=\"tp_pub_additional_pages\">pp. 2322, <\/span><span class=\"tp_pub_additional_year\">2020<\/span><span class=\"tp_pub_additional_note\">, (JCR (2018): 2.217)<\/span>.<\/span><\/div><!--more--><\/p>\n<h2 class=\"tp_abstract\">Resumen<\/h2><p class=\"tp_abstract\"><\/p>\n<h2 class=\"tp_links\">Enlaces<\/h2><p class=\"tp_abstract\"><ul class=\"tp_pub_list\"><li><i class=\"ai ai-doi\"><\/i><a class=\"tp_pub_list\" href=\"https:\/\/dx.doi.org\/10.3390\/app10072322\" title=\"DOI de seguimiento:10.3390\/app10072322\" target=\"_blank\">doi:10.3390\/app10072322<\/a><\/li><\/ul><\/p>\n<h2 class=\"tp_bibtex\">BibTeX (<a href=\"https:\/\/grupo.us.es\/minerva?feed=tp_pub_bibtex&amp;key=Pedro2020\">Download<\/a>)<\/h2><pre class=\"tp_bibtex\">@article{Pedro2020,\r\ntitle = {Temporal Convolutional Networks Applied to Energy-related Time Series Forecasting},\r\nauthor = {P Lara-Ben\u00edtez and M Carranza-Garc\u00eda and J M Luna-Romera and J C Riquelme},\r\ndoi = {10.3390\/app10072322},\r\nyear  = {2020},\r\ndate = {2020-03-28},\r\njournal = {Applied Sciences},\r\nvolume = {10},\r\nnumber = {7},\r\npages = {2322},\r\nnote = {JCR (2018): 2.217},\r\nkeywords = {machine learning},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-473","page","type-page","status-publish","hentry","post-item clearfix"],"_links":{"self":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/pages\/473","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/comments?post=473"}],"version-history":[{"count":0,"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/pages\/473\/revisions"}],"wp:attachment":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/media?parent=473"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}