{"id":616,"date":"2016-07-18T18:16:51","date_gmt":"2016-07-18T21:16:51","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=616"},"modified":"2016-07-18T18:16:51","modified_gmt":"2016-07-18T21:16:51","slug":"vol10-no3-art5","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/","title":{"rendered":"Echo State Networks in Seasonal Streamflow Series Prediction"},"content":{"rendered":"

T\u00edtulo:<\/strong> Echo State Networks in Seasonal Streamflow Series Prediction<\/p>\n

Autores:<\/strong> Siqueira, Hugo Valadares; Boccato, Levy; Attux, Romis; Lyra Filho, Christiano<\/p>\n

Resumo:<\/strong> Echo state networks (ESNs) are promising options for performing time series prediction, as they establish an attractive tradeoff between dynamical processing capability and simplicity in the training process. The recurrent character of these structures is essentially concentrated on a nonlinear processing stage known as dynamic reservoir, whereas the output layer \u2013 also termed readout \u2013 is allowed to assume the form of a combiner that is, as a rule, linear with respect to its free parameters. Recently, Butcher et al. and Boccato et al. proposed readout design paradigms \u2013 based, respectively, on Volterra filtering and extreme learning machines \u2013 that were shown to enhance the information extraction potential of ESNs without compromising the intrinsic simplicity of their training process. In this work, these approaches are comparatively analyzed, using different reservoir configurations, in the context of monthly streamflow series forecasting. The analysis is based on data from the hydroelectric plant of Furnas, covering three different periods with distinct hydrologic profiles and including the canonical linear approach for readout design as a benchmark. The results reveal that the aforementioned proposals may lead to a relevant performance improvement, thus indicating that the reservoir is not per se capable of fully exploring the nonlinear relationships underlying the focused data.<\/p>\n

Palavras-chave:<\/strong> Echo State Networks; Volterra Filtering; PCA; Extreme Learning Machine; Seasonal Streamflow Series Forecasting<\/p>\n

P\u00e1ginas:<\/strong> 11<\/p>\n

C\u00f3digo DOI:<\/strong> 10.21528\/lmln-vol10-no3-art5<\/a><\/p>\n

Artigo em PDF:<\/strong> vol10-no3-art5.pdf<\/a><\/p>\n

Arquivo BibTex:<\/strong> vol10-no3-art5.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

T\u00edtulo: Echo State Networks in Seasonal Streamflow Series Prediction Autores: Siqueira, Hugo Valadares; Boccato, Levy; Attux, Romis; Lyra Filho, Christiano Resumo: Echo state networks (ESNs) are promising options for performing time series prediction, as they establish an attractive tradeoff between Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":603,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-616","page","type-page","status-publish","hentry"],"yoast_head":"\nEcho State Networks in Seasonal Streamflow Series Prediction - 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\/vol10-no3\/vol10-no3-art5\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Echo State Networks in Seasonal Streamflow Series Prediction - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Echo State Networks in Seasonal Streamflow Series Prediction Autores: Siqueira, Hugo Valadares; Boccato, Levy; Attux, Romis; Lyra Filho, Christiano Resumo: Echo state networks (ESNs) are promising options for performing time series prediction, as they establish an attractive tradeoff between Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. tempo de leitura\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minuto\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/\",\"name\":\"Echo State Networks in Seasonal Streamflow Series Prediction - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"datePublished\":\"2016-07-18T21:16:51+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning & Nonlinear Models – L&NLM – Volume 10 – N\u00famero 3\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Echo State Networks in Seasonal Streamflow Series Prediction\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Echo State Networks in Seasonal Streamflow Series Prediction - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art5\/","og_locale":"pt_BR","og_type":"article","og_title":"Echo State Networks in Seasonal Streamflow Series Prediction - Learning and NonLinear Models","og_description":"T\u00edtulo: Echo State Networks in Seasonal Streamflow Series Prediction Autores: Siqueira, Hugo Valadares; 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