{"id":595,"date":"2016-07-18T17:54:31","date_gmt":"2016-07-18T20:54:31","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=595"},"modified":"2016-07-18T17:54:31","modified_gmt":"2016-07-18T20:54:31","slug":"vol10-no2-art3","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol10-no2\/vol10-no2-art3\/","title":{"rendered":"S\u00e9rie Temporal Fuzzy com \u201cn\u201d Diferen\u00e7as para Identifica\u00e7\u00e3o da Ordem do Modelo Via Fun\u00e7\u00e3o de Autocorrela\u00e7\u00e3o"},"content":{"rendered":"

T\u00edtulo:<\/strong> S\u00e9rie Temporal Fuzzy com \u201cn\u201d Diferen\u00e7as para Identifica\u00e7\u00e3o da Ordem do Modelo Via Fun\u00e7\u00e3o de Autocorrela\u00e7\u00e3o<\/p>\n

Autores:<\/strong> Carvalho Junior, Jos\u00e9 Gracildo de; Costa Junior, Carlos Tavares da; Lago Neto, Jo\u00e3o Caldas do<\/p>\n

Resumo:<\/strong> A fun\u00e7\u00e3o de autocorrela\u00e7\u00e3o possui uma grande capacidade quanto \u00e0 identifica\u00e7\u00e3o da ordem de um modelo de s\u00e9rie temporal, a partir das estimativas das correla\u00e7\u00f5es e covari\u00e2ncias dos dados, neste sentido, esta fun\u00e7\u00e3o determina modelos autoregressivos sazonais e subconjuntos de maneira eficiente mediante a fun\u00e7\u00e3o de autocorrela\u00e7\u00e3o parcial, al\u00e9m de indicar se os dados s\u00e3o provenientes de um modelo n\u00e3o estacion\u00e1rio ou quase n\u00e3o estacion\u00e1rio. Assim, este trabalho utilizou o calculo da fun\u00e7\u00e3o de autocorrela\u00e7\u00e3o amostral e parcial, para os conjuntos de dados fuzzy apresentados por (Song, 2003), e a partir dos resultados destas fun\u00e7\u00f5es se fez \u00e0 op\u00e7\u00e3o pela ordem ideal do modelo de s\u00e9rie temporal fuzzy a ser adotado. Obteve-se ainda, uma medida de depend\u00eancia entre os conjuntos de dados fuzzy, mediante a m\u00e9dia dos valores da fun\u00e7\u00e3o de autocorrela\u00e7\u00e3o amostral e parcial conjunta calculada para diferentes conjuntos de dados.<\/p>\n

Palavras-chave:<\/strong> Fun\u00e7\u00e3o de autocorrela\u00e7\u00e3o fuzzy; Constru\u00e7\u00e3o de modelos de s\u00e9rie temporal fuzzy<\/p>\n

P\u00e1ginas:<\/strong> 10<\/p>\n

C\u00f3digo DOI:<\/strong> 10.21528\/lmln-vol10-no2-art3<\/a><\/p>\n

Artigo em PDF:<\/strong> vol10-no2-art3.pdf<\/a><\/p>\n

Arquivo BibTex:<\/strong> vol10-no2-art3.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

T\u00edtulo: S\u00e9rie Temporal Fuzzy com \u201cn\u201d Diferen\u00e7as para Identifica\u00e7\u00e3o da Ordem do Modelo Via Fun\u00e7\u00e3o de Autocorrela\u00e7\u00e3o Autores: Carvalho Junior, Jos\u00e9 Gracildo de; Costa Junior, Carlos Tavares da; Lago Neto, Jo\u00e3o Caldas do Resumo: A fun\u00e7\u00e3o de autocorrela\u00e7\u00e3o possui uma Read More …<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":585,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-595","page","type-page","status-publish","hentry"],"yoast_head":"\nS\u00e9rie Temporal Fuzzy com \u201cn\u201d Diferen\u00e7as para Identifica\u00e7\u00e3o da Ordem do Modelo Via Fun\u00e7\u00e3o de Autocorrela\u00e7\u00e3o - 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-no2\/vol10-no2-art3\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"S\u00e9rie Temporal Fuzzy com \u201cn\u201d Diferen\u00e7as para Identifica\u00e7\u00e3o da Ordem do Modelo Via Fun\u00e7\u00e3o de Autocorrela\u00e7\u00e3o - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: S\u00e9rie Temporal Fuzzy com \u201cn\u201d Diferen\u00e7as para Identifica\u00e7\u00e3o da Ordem do Modelo Via Fun\u00e7\u00e3o de Autocorrela\u00e7\u00e3o Autores: Carvalho Junior, Jos\u00e9 Gracildo de; 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