{"id":607,"date":"2016-07-18T18:12:36","date_gmt":"2016-07-18T21:12:36","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=607"},"modified":"2016-07-18T18:12:36","modified_gmt":"2016-07-18T21:12:36","slug":"vol10-no3-art1","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art1\/","title":{"rendered":"Evolving Functional Fuzzy Model for Term Structure of Interest Rates Forecasting"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Evolving Functional Fuzzy Model for Term Structure of Interest Rates Forecasting<\/p>\n<p><strong>Autores:<\/strong> Maciel, Leandro; Gomide, Fernando; Ballini, Rosangela<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> Evolving fuzzy systems use data streams to continuously adapt the structure and functionality of fuzzy rule-based models. It gradually develops the model structure and its parameters from a stream of data, which is essential when dealing with complex and nonstationary systems. In this paper, we suggest the use of functional evolving fuzzy modeling in the form of Takagi-Sugeno (eTS) model to forecast Brazilian government bond yields through the Nelson-Siegel function. In this case the eTS adaptively estimates the parameters Nelson-Siegel function to perform forecasts. This is a crucial procedure for bond portfolio management, derivatives and bonds pricing. The experiments reported here use daily data of the Brazilian National Treasury Bills of the period from January 2007 to December 2009 for one, three, six, nine and twelve months ahead forecasting horizons. The evolving model was compared with autoregressive and random walk models, in terms of root mean squared error. Results indicate that eTS is a promising approach to deal with government bond yields forecasting because it gives more accurate Nelson-Siegel parameters values than traditional approaches.<\/p>\n<p><strong>Palavras-chave:<\/strong> Evolving Fuzzy Systems; Time Series Forecasting; Yield Curve; Interest Rate<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 9<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol10-no3-art1\">10.21528\/lmln-vol10-no3-art1<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no3-art1.pdf\" rel=\"\">vol10-no3-art1.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol10-no3-art1.bib\" rel=\"\">vol10-no3-art1.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Evolving Functional Fuzzy Model for Term Structure of Interest Rates Forecasting Autores: Maciel, Leandro; Gomide, Fernando; Ballini, Rosangela Resumo: Evolving fuzzy systems use data streams to continuously adapt the structure and functionality of fuzzy rule-based models. It gradually develops <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no3\/vol10-no3-art1\/\" class=\"read-more\">Read More &#8230;<\/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-607","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 Functional Fuzzy Model for Term Structure of Interest Rates 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\/vol10-no3\/vol10-no3-art1\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Evolving Functional Fuzzy Model for Term Structure of Interest Rates Forecasting - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Evolving Functional Fuzzy Model for Term Structure of Interest Rates Forecasting Autores: Maciel, Leandro; Gomide, Fernando; Ballini, Rosangela Resumo: Evolving fuzzy systems use data streams to continuously adapt the structure and functionality of fuzzy rule-based models. 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