{"id":68301,"date":"2018-03-09T22:56:45","date_gmt":"2018-03-09T22:56:45","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/metodos-de-prediccion-para-series-temporales-de-intervalos-e-histogramas\/"},"modified":"2018-03-09T22:56:45","modified_gmt":"2018-03-09T22:56:45","slug":"metodos-de-prediccion-para-series-temporales-de-intervalos-e-histogramas","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/inteligencia-artificial\/metodos-de-prediccion-para-series-temporales-de-intervalos-e-histogramas\/","title":{"rendered":"M\u00e9todos de predicci\u00f3n para series temporales de intervalos e histogramas"},"content":{"rendered":"

Tesis doctoral de Javier Arroyo Gallardo <\/strong><\/h2>\n

Las series temporales cl\u00e1sicas, donde cada instante es descrito por un n\u00famero real, sirven para representar una gran multitud de situaciones de la vida real, pero no son capaces de describir fielmente situaciones en las que en cada instante se deba reflejar cierta variabilidad. Los datos simb\u00f3licos de intervalo e histograma permiten representar dicha variabilidad a lo largo del tiempo, dando lugar a series temporales de intervalos e histogramas, respectivamente. En este trabajo se han abordado algunos aspectos relativos a estas series temporales como, por ejemplo, la medici\u00f3n del error, pero el principal objetivo de ha sido el desarrollar m\u00e9todos que permitan predecir estos nuevos tipos de series temporales de manera eficaz. las aproximaciones que se han propuesto para predecir series temporales de intervalos incluyen: – alisados exponenciales basados en la aritm\u00e9tica de intervalos – m\u00e9todo de k-nn basado en la aritm\u00e9tica de intervalos – perceptr\u00f3n multicapa basado en la aritm\u00e9tica de intervalos – predicci\u00f3n mediante las series temporales de sus componentes (m\u00ednimo, m\u00e1ximo, centro y radio) aplicando para ello m\u00e9todos de predicci\u00f3n (univariantes o multivariantes) para series temporales cl\u00e1sicas. mientras que los m\u00e9todos desarrollados para predecir series temporales de histogramas son: – alisados exponenciales basados en la aritm\u00e9tica de histogramas – alisados exponenciales basados en el concepto de baricentro – m\u00e9todo de k-nn basado en el histograma basados en el concepto de baricentro la capacidad predictiva de tods estos m\u00e9todos ha sido probada con \u00e9xito en ejemplos reales de diferentes \u00e1mbitos como, por ejemplo, la meteorolog\u00eda o las finanzas.<\/p>\n

 <\/p>\n

Datos acad\u00e9micos de la tesis doctoral \u00abM\u00e9todos de predicci\u00f3n para series temporales de intervalos e histogramas<\/strong>\u00ab<\/h3>\n
    \n
  • T\u00edtulo de la tesis:<\/strong>\u00a0 M\u00e9todos de predicci\u00f3n para series temporales de intervalos e histogramas <\/li>\n
  • Autor:<\/strong>\u00a0 Javier Arroyo Gallardo <\/li>\n
  • Universidad:<\/strong>\u00a0 Pontificia comillas<\/li>\n
  • Fecha de lectura de la tesis:<\/strong>\u00a0 28\/11\/2008<\/li>\n<\/ul>\n

     <\/p>\n

    Direcci\u00f3n y tribunal<\/h3>\n
      \n
    • Director de la tesis<\/strong>\n
        \n
      • Carlos Mat\u00e9 Jim\u00e9nez<\/li>\n<\/ul>\n<\/li>\n
      • Tribunal<\/strong>\n
          \n
        • Presidente del tribunal: vicente Quesada paloma <\/li>\n
        • ismael S\u00e1nchez rodr\u00edguez-morcillo (vocal)<\/li>\n
        • alfonso Garc\u00eda p\u00e9rez (vocal)<\/li>\n
        • M\u00aa paula de pinho Brito duarte silva (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

           <\/p>\n","protected":false},"excerpt":{"rendered":"

          Tesis doctoral de Javier Arroyo Gallardo Las series temporales cl\u00e1sicas, donde cada instante es descrito por un n\u00famero real, sirven […]<\/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":[2528,18397,11527,4030],"tags":[23101,23862,150006,150005,150007,3367],"class_list":["post-68301","post","type-post","status-publish","format-standard","hentry","category-inteligencia-artificial","category-pontificia-comillas","category-series-temporales","category-tecnicas-de-prediccion-estadistica","tag-alfonso-garcia-perez","tag-carlos-mate-jimenez","tag-ismael-sanchez-rodriguez-morcillo","tag-javier-arroyo-gallardo","tag-ma-paula-de-pinho-brito-duarte-silva","tag-vicente-quesada-paloma"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/68301","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=68301"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/68301\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=68301"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=68301"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=68301"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}