{"id":82780,"date":"2000-01-01T00:00:00","date_gmt":"2000-01-01T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/time-series-prediction-using-inductive-reasoning-techniques\/"},"modified":"2000-01-01T00:00:00","modified_gmt":"2000-01-01T00:00:00","slug":"time-series-prediction-using-inductive-reasoning-techniques","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/time-series-prediction-using-inductive-reasoning-techniques\/","title":{"rendered":"Time series prediction using inductive reasoning techniques."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Josefina Lopez Herrera <\/strong><\/h2>\n<p>En esta tesis se describen los nuevos elementos introducidos en la metodolog\u00eda fuzzy inductive reasoning (fir) que permite predecir el comportamiento futuro de series temporales. En la identificaci\u00f3n de sistemas se hab\u00edan obtenido muy buenos resultados al utilizar esta metodolog\u00eda. Por ello se decidi\u00f3 evaluar esta metodolog\u00eda en el campo del an\u00e1lisis de series temporales que es un asunto m\u00e1s complejo a causa de la imposibilidad de excitar las entradas de los sistemas que las generan.  para saber si esta metodolog\u00eda es v\u00e1lida en elcampo de an\u00e1lisis de series temporales se hizo un estudio comparativo con otras metodolog\u00edas como son las conexionistas, las que utilizan modelos lineales y no lineales. Esto permiti\u00f3 caracterizar el tipo de series temporales que mejor predice fir. Se muestra que esta metodolog\u00eda explota toda la informaci\u00f3n contenida en los datos disponibles de las series temporales quasi-estacionarias con elementos deterministas, sin necesidad de emplear variables cualitativas generadas desde la misma se\u00f1al como son las derivadas cualitativas.  a causa de la naturaleza cualitativa de la metodolog\u00eda, en un inicio se produjeron predicciones ambiguas. Para superar las dificultades se incorporaron nuevos elementos de predicci\u00f3n. Se modificaron las f\u00f3rmulas para calcular la distancia relativa y los pesos absolutos de los cinco vecinos m\u00e1s cercanos y se incorporaron nuevas medidas de confianza, similitud y proximidad, que permiten evaluar el error de predicci\u00f3n sin necesidad de conocer el valor real. La medida de proximidad se basa en la funci\u00f3n de la distancia, mientras que la similitud est\u00e1 basada en la similitud de conjuntos borrosos. Se utiliza una generalizaci\u00f3n de la funci\u00f3n cl\u00e1sica de equiValencia basada en las definiciones de cardinalidad y diferencia de la teor\u00eda de conjuntos borrosos. Originalmente fue propuesta por dubois y prad\u00e9.  se desarrollaron dos nuevos t\u00e9cnicas de predic<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Time series prediction using inductive reasoning techniques.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Time series prediction using inductive reasoning techniques. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Josefina Lopez Herrera <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Polit\u00e9cnica de catalunya<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 01\/01\/2000<\/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>Francoise Cellier<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Luis Bas\u00e1\u00f1ez villaluenga <\/li>\n<li>josep Aguilar martin (vocal)<\/li>\n<li>lennart Ljung (vocal)<\/li>\n<li>pilar Mu\u00f1oz (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Josefina Lopez Herrera En esta tesis se describen los nuevos elementos introducidos en la metodolog\u00eda fuzzy inductive [&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 center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[1890,1477,2528,126,15596,11527],"tags":[175811,175810,15687,175812,46812,175813],"class_list":["post-82780","post","type-post","status-publish","format-standard","hentry","category-ciencia-de-los-ordenadores","category-estadistica","category-inteligencia-artificial","category-matematicas","category-politecnica-de-catalunya","category-series-temporales","tag-francoise-cellier","tag-josefina-lopez-herrera","tag-josep-aguilar-martin","tag-lennart-ljung","tag-luis-basanez-villaluenga","tag-pilar-munoz"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/82780","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=82780"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/82780\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=82780"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=82780"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=82780"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}