{"id":1330,"date":"2021-01-19T13:30:47","date_gmt":"2021-01-19T15:30:47","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1330"},"modified":"2021-01-19T13:30:47","modified_gmt":"2021-01-19T15:30:47","slug":"vol18-no2-art4","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol18-no2\/vol18-no2-art4\/","title":{"rendered":"Evolving deep neural networks for Time Series Forecasting"},"content":{"rendered":"<p>L\u00eddio Mauro Lima de Campos <a href=\"http:\/\/orcid.org\/0000-0003-4315-829X\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Jherson Haryson Almeida Pereira, Danilo Souza Duarte <a href=\"http:\/\/orcid.org\/0000-0001-9094-3392\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a> &amp; Roberto C\u00e9lio Lim\u00e3o de Oliveira <a href=\"http:\/\/orcid.org\/0000-0002-6640-3182\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Resumo:<\/strong> O objetivo deste artigo \u00e9 introduzir uma abordagem inspirada biologicamente que possa gerar automaticamente redes neurais profundas com boa capacidade de previs\u00e3o, menor erro e grande toler\u00e2ncia a ru\u00eddos. Para tanto, foram utilizados tr\u00eas paradigmas biol\u00f3gicos: Algoritmo Gen\u00e9tico (GA), Sistema Lindenmayer e Redes Neurais. As se\u00e7\u00f5es finais do artigo apresentam alguns experimentos com o objetivo de investigar as possibilidades do m\u00e9todo na previs\u00e3o do pre\u00e7o da energia no mercado brasileiro. O modelo proposto considera uma previs\u00e3o de pre\u00e7o em v\u00e1rias etapas (12, 24 e 36 semanas a frente). Os resultados para redes MLP e LSTM mostram boa capacidade de prever picos e precis\u00e3o satisfat\u00f3ria de acordo com medidas de erro comparadas com outros m\u00e9todos.<\/p>\n<p><strong>Palavras-chave:<\/strong> NeuroEvolu\u00e7\u00e3o, Previs\u00e3o de s\u00e9ries temporais; redes neurais recorrentes, redes LSTM.<\/p>\n<p><strong>Abstract:<\/strong> The aim of this paper is to introduce a biologically inspired approach that can automatically generate Deep Neural networks with good prediction capacity, smaller error and large tolerance to noises. In order to do this, three biological paradims were used: Genetic Algorithm (GA), Lindenmayer System and Neural Networks (DNNs). The final sections of the paper presents some experiments aimed at investigating the possibilities of the method in forecast the price of energy in the Brazilian market. The proposed model considers a multi-step ahead price prediction (12, 24, and 36 weeks-ahead). The results for MLP and LSTM networks show good ability to predict peaks and satisfactory accuracy according to error measures comparing with other methods.<\/p>\n<p><strong>Keywords:<\/strong> NeuroEvolution; Time series forecasting; recurrent neural networks, LSTM networks.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol18-no2-art4\">10.21528\/lnlm-vol18-no2-art4<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2021\/01\/vol18-no2-art4.pdf\">vol18-no2-art4.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2021\/01\/vol18-no2-art4.bib\">vol18-no2-art4.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>L\u00eddio Mauro Lima de Campos , Jherson Haryson Almeida Pereira, Danilo Souza Duarte &amp; Roberto C\u00e9lio Lim\u00e3o de Oliveira Resumo: O objetivo deste artigo \u00e9 introduzir uma abordagem inspirada biologicamente que possa gerar automaticamente redes neurais profundas com boa capacidade <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol18-no2\/vol18-no2-art4\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1306,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1330","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Evolving deep neural networks for Time Series Forecasting - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol18-no2\/vol18-no2-art4\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Evolving deep neural networks for Time Series Forecasting - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"L\u00eddio Mauro Lima de Campos , Jherson Haryson Almeida Pereira, Danilo Souza Duarte &amp; 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