{"id":67285,"date":"2008-03-10T00:00:00","date_gmt":"2008-03-10T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/ana-lisi-de-sa%c2%a8ries-temporals-mitjana%c2%a7ant-xarxes-neuronals-artificials\/"},"modified":"2008-03-10T00:00:00","modified_gmt":"2008-03-10T00:00:00","slug":"ana-lisi-de-sa%c2%a8ries-temporals-mitjana%c2%a7ant-xarxes-neuronals-artificials","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/barcelona\/ana-lisi-de-sa%c2%a8ries-temporals-mitjana%c2%a7ant-xarxes-neuronals-artificials\/","title":{"rendered":"An\u00c1\u00a0lisi de s\u00e9ries temporals mitjan\u00c1\u00a7ant xarxes neuronals artificials"},"content":{"rendered":"<h2>Tesis doctoral de <strong> Esteve Xavier Rif\u00c1\u00a0 Ros <\/strong><\/h2>\n<p>La teor\u00eda de sistemas din\u00e1micos (tsd) proporciona herramientas para el an\u00e1lisis de series temporales (st). La predicci\u00f3n no lineal de st permite extraer alguna de las caracter\u00edsticas que esta teor\u00eda propone, como la dimensi\u00f3n de inmersi\u00f3n (di) o la sensibilidad a las condiciones iniciales (sci). Sugihara y may (1990) han difundido un m\u00e9todo a tal efecto utilizando una forma de predicci\u00f3n no param\u00e9trica que basa sus decisiones mediante la observaci\u00f3n de gr\u00e1ficos, procedimiento que a nuestro entender a\u00f1ade un componente de subjetividad no deseado. Para superar esta dificultad propongo realizar la toma de decisiones en base a la inferencia estad\u00edstica. El m\u00e9todo que expongo en esta tesis se basa en la predicci\u00f3n no lineal mediante redes neuronales artificiales (rna). Se han realizado experimentos de simulaci\u00f3n para estimar la di y evaluar la sci entrenando rna. En el primer caso se pretend\u00eda encontrar un invariante en la predicci\u00f3n en funci\u00f3n del n\u00famero de componentes del atractor reconstruido. \u00e9ste coincide con el valor de la di en el que la predicci\u00f3n ya no mejora aunque aumentemos este n\u00famero. En el segundo caso, una vez entrenada cada rna, se analiz\u00f3 si exist\u00eda una disminuci\u00f3n estad\u00edsticamente significativa de la precisi\u00f3n en la predicci\u00f3n en funci\u00f3n del n\u00famero de iteraciones. En caso de existir \u00e9sta se concluir\u00eda que la st posee sci. Para probar esta t\u00e9cnica se han utilizado st simuladas (componente x del mapa de h\u00e9non y del atractor de r\u00ed\u00b6ssler) libres de ruido y con dos niveles de ruido distinto. La aplicaci\u00f3n de la t\u00e9cnica ha permitido estimar la di y la sci de los tres conjuntos de los datos del mapa de h\u00e9non llegando a los resultados esperados. Respecto de los datos del atractor de r\u00ed\u00b6ssler se dan algunas desviaciones respecto de los resultados esperados, especialmente en lo que respecta a la evaluaci\u00f3n de la sci.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>An\u00c1\u00a0lisi de s\u00e9ries temporals mitjan\u00c1\u00a7ant xarxes neuronals artificials<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 An\u00c1\u00a0lisi de s\u00e9ries temporals mitjan\u00c1\u00a7ant xarxes neuronals artificials <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Esteve Xavier Rif\u00c1\u00a0 Ros <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Barcelona<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 03\/10\/2008<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Manel Viader Junyent<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: carles enric Riba  campos <\/li>\n<li>vicen\u00ed\u00a7 Quera jordana (vocal)<\/li>\n<li>albert Fornieles deu (vocal)<\/li>\n<li>josep Marco pallares (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Esteve Xavier Rif\u00c1\u00a0 Ros La teor\u00eda de sistemas din\u00e1micos (tsd) proporciona herramientas para el an\u00e1lisis de series [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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