{"id":753,"date":"2024-01-01T12:00:00","date_gmt":"2024-01-01T12:00:00","guid":{"rendered":"https:\/\/grupo.us.es\/minerva\/from-simple-to-complex-a-sequential-method-for-enhancing-time-series-forecasting-with-deep-learning\/"},"modified":"2024-01-01T12:00:00","modified_gmt":"2024-01-01T12:00:00","slug":"from-simple-to-complex-a-sequential-method-for-enhancing-time-series-forecasting-with-deep-learning","status":"publish","type":"page","link":"https:\/\/grupo.us.es\/minerva\/from-simple-to-complex-a-sequential-method-for-enhancing-time-series-forecasting-with-deep-learning\/","title":{"rendered":"From simple to complex: a sequential method for enhancing time series forecasting with deep learning"},"content":{"rendered":"<p><div class=\"tp_single_publication\"><span class=\"tp_single_author\">M J Jim\u00e9nez-Navarro, M Mart\u00ednez-Ballesteros, F Mart\u00ednez-\u00c1lvarez, A Troncoso, G Asencio-Cort\u00e9s: <\/span> <span class=\"tp_single_title\">From simple to complex: a sequential method for enhancing time series forecasting with deep learning<\/span>. <span class=\"tp_single_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>.<\/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=\"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><\/p>\n<h2 class=\"tp_bibtex\">BibTeX (<a href=\"https:\/\/grupo.us.es\/minerva?feed=tp_pub_bibtex&amp;key=10.1093\/jigpal\/jzae030\">Download<\/a>)<\/h2><pre class=\"tp_bibtex\">@article{10.1093\/jigpal\/jzae030,\r\ntitle = {From simple to complex: a sequential method for enhancing time series forecasting with deep learning},\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},\r\nurl = {https:\/\/doi.org\/10.1093\/jigpal\/jzae030},\r\ndoi = {10.1093\/jigpal\/jzae030},\r\nyear  = {2024},\r\ndate = {2024-01-01},\r\njournal = {Logic Journal of the IGPL},\r\nvolume = {N\/A},\r\npages = {N\/A},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-753","page","type-page","status-publish","hentry","post-item clearfix"],"_links":{"self":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/pages\/753","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=753"}],"version-history":[{"count":0,"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/pages\/753\/revisions"}],"wp:attachment":[{"href":"https:\/\/grupo.us.es\/minerva\/wp-json\/wp\/v2\/media?parent=753"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}