{"id":164810,"date":"2022-08-10T10:36:00","date_gmt":"2022-08-10T09:36:00","guid":{"rendered":"https:\/\/datascientest.com\/es\/?p=164810"},"modified":"2023-10-30T17:21:05","modified_gmt":"2023-10-30T16:21:05","slug":"statsmodels-todo-acerca","status":"publish","type":"post","link":"https:\/\/datascientest.com\/es\/statsmodels-todo-acerca","title":{"rendered":"Statsmodels : todo acerca de la biblioteca de Python"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"164810\" class=\"elementor elementor-164810\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4952e35 e-flex e-con-boxed e-con\" data-id=\"4952e35\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-feb1791 elementor-widget elementor-widget-text-editor\" data-id=\"feb1791\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<h2><b>Si alguna vez has intentado modelar series temporales, probablemente habr\u00e1s o\u00eddo hablar de los modelos ARMA o ARIMA. En Python la biblioteca m\u00e1s utilizada para esto es statsmodels. En este art\u00edculo presentaremos r\u00e1pidamente los usos de statsmodels y algunos ejemplos de uso.<\/b><\/h2>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-df5b850 e-flex e-con-boxed e-con\" data-id=\"df5b850\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-2261a6d elementor-widget elementor-widget-heading\" data-id=\"2261a6d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">\u00bfQu\u00e9 hacer con statsmodels?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-823b8bb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"823b8bb\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-72838c3\" data-id=\"72838c3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8f307c6 elementor-widget elementor-widget-text-editor\" data-id=\"8f307c6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Statsmodels es una biblioteca <\/span><b>de an\u00e1lisis y modelado de datos estad\u00edsticos<\/b><span style=\"font-weight: 400;\">. Ofrece una serie de funcionalidades que no se encuentran en bibliotecas m\u00e1s tradicionales como<\/span><a href=\"https:\/\/scikit-learn.org\/stable\/\"> <b>sklearn<\/b><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><b>Regresi\u00f3n lineal<\/b><\/p><p><span style=\"font-weight: 400;\">En primer lugar, statsmodels completa en gran medida la <\/span><b>regresi\u00f3n lineal<\/b><span style=\"font-weight: 400;\"> cl\u00e1sica proponiendo nuevos estimadores de <\/span><b>m\u00ednimos cuadrados<\/b><span style=\"font-weight: 400;\">. Normalmente, se utilizan los m\u00ednimos cuadrados ordinarios (OLS) para estimar la regresi\u00f3n lineal. Sin embargo, en el caso de que algunos residuos est\u00e9n correlacionados, la regresi\u00f3n lineal deja de ser eficaz. Los <\/span><b>m\u00ednimos cuadrados generalizados<\/b><span style=\"font-weight: 400;\"> (GLS) pueden entonces ayudar a resolver este problema.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-aa8c3e3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"aa8c3e3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4556f52\" data-id=\"4556f52\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b8f5f8a elementor-widget elementor-widget-image\" data-id=\"b8f5f8a\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"613\" height=\"460\" src=\"https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0.png\" class=\"attachment-large size-large wp-image-40628\" alt=\"\" srcset=\"https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0.png 613w, https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-300x225.png 300w\" sizes=\"(max-width: 613px) 100vw, 613px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Ejemplo de regresi\u00f3n lineal OLS<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3fab805 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3fab805\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bbc4898\" data-id=\"bbc4898\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5fd6c8f elementor-widget elementor-widget-text-editor\" data-id=\"5fd6c8f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Statsmodels es una biblioteca <\/span><b>de an\u00e1lisis y modelado de datos estad\u00edsticos<\/b><span style=\"font-weight: 400;\">. Ofrece una serie de funcionalidades que no se encuentran en bibliotecas m\u00e1s tradicionales como<\/span><a href=\"https:\/\/scikit-learn.org\/stable\/\"> sklearn<\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><b>Regresi\u00f3n lineal<\/b><\/p><p><span style=\"font-weight: 400;\">En primer lugar, statsmodels completa en gran medida la <\/span><b>regresi\u00f3n lineal<\/b><span style=\"font-weight: 400;\"> cl\u00e1sica proponiendo nuevos estimadores de <\/span><b>m\u00ednimos cuadrados<\/b><span style=\"font-weight: 400;\">. Normalmente, se utilizan los m\u00ednimos cuadrados ordinarios (OLS) para estimar la regresi\u00f3n lineal. Sin embargo, en el caso de que algunos residuos est\u00e9n correlacionados, la regresi\u00f3n lineal deja de ser eficaz. Los <\/span><b>m\u00ednimos cuadrados generalizados<\/b><span style=\"font-weight: 400;\"> (GLS) pueden entonces ayudar a resolver este problema.<\/span><\/p><p><span style=\"font-weight: 400;\">Paralelamente a este m\u00e9todo, statsmodels presenta otros dos estimadores de m\u00ednimos cuadrados ordinarios m\u00e1s complejos : m\u00ednimos cuadrados recursivos (RLS) y m\u00ednimos cuadrados m\u00f3viles (MLS).<\/span><\/p><p><span style=\"font-weight: 400;\">Adem\u00e1s, la regresi\u00f3n lineal cl\u00e1sica impone <\/span><b>una serie de supuestos<\/b><span style=\"font-weight: 400;\"> que deben comprobarse durante el proceso. Statsmodels presenta diferentes <\/span><b>pruebas estad\u00edsticas<\/b><span style=\"font-weight: 400;\"> que permiten comprobar :<\/span><\/p><ul><li><span style=\"font-weight: 400;\">La no multicolinealidad de las variables<\/span><\/li><li><span style=\"font-weight: 400;\">La homocedasticidad (prueba de Breusch-Pagan)<\/span><\/li><li><span style=\"font-weight: 400;\">La normalidad de los residuos (prueba de Jarque-Bera)<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">Veamos un ejemplo de aplicaci\u00f3n en<a href=\"https:\/\/datascientest.com\/es\/python-o-r-que-elegir\"> Python<\/a> :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4ffd96c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4ffd96c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-28c154e\" data-id=\"28c154e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-02a6f36 elementor-widget elementor-widget-shortcode\" data-id=\"02a6f36\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/Tech-Netiums\/7be026ca9bbe56d17f28a5324614a597.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a002aa2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a002aa2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8409a1d\" data-id=\"8409a1d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-98beb3b elementor-widget elementor-widget-heading\" data-id=\"98beb3b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Modelos lineales generalizados<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-17462c0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"17462c0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d31ad1c\" data-id=\"d31ad1c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f35a19e elementor-widget elementor-widget-text-editor\" data-id=\"f35a19e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Paralelamente, la biblioteca statsmodels permite utilizar <\/span><b>modelos lineales generalizados (GLM)<\/b><span style=\"font-weight: 400;\">, que son una generalizaci\u00f3n de la regresi\u00f3n lineal cl\u00e1sica.<\/span><\/p><p><span style=\"font-weight: 400;\">De hecho, la regresi\u00f3n lineal supone que la variable en cuesti\u00f3n sigue una distribuci\u00f3n normal (supuesto impuesto por la normalidad de los residuos). En el caso del GLM, se puede utilizar cualquier distribuci\u00f3n de la familia exponencial.<\/span><\/p><p><span style=\"font-weight: 400;\">Adem\u00e1s, el GLM vincula la variable en cuesti\u00f3n con el modelo a trav\u00e9s de una <\/span><b>funci\u00f3n de enlace<\/b><span style=\"font-weight: 400;\">, lo que permite que la regresi\u00f3n lineal cl\u00e1sica sea a\u00fan m\u00e1s flexible.<\/span><\/p><p><span style=\"font-weight: 400;\">Un caso concreto de modelo lineal generalizado es la regresi\u00f3n log\u00edstica. La variable en cuesti\u00f3n sigue <\/span><b>una distribuci\u00f3n binomial<\/b><span style=\"font-weight: 400;\"> y se utiliza una funci\u00f3n de enlace <\/span><b>logit<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-11fe105 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"11fe105\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d8e14e5\" data-id=\"d8e14e5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d01c72f elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"d01c72f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/es\/formacion-data-scientist\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">M\u00e1s informaci\u00f3n sobre el curso de Data Scientist<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d81269 elementor-widget elementor-widget-image\" data-id=\"2d81269\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"640\" height=\"384\" src=\"https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-1.png\" class=\"attachment-large size-large wp-image-40637\" alt=\"\" srcset=\"https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-1.png 640w, https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-1-300x180.png 300w\" sizes=\"(max-width: 640px) 100vw, 640px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Ejemplo de regresi\u00f3n log\u00edstica<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-978b2d7 elementor-widget elementor-widget-shortcode\" data-id=\"978b2d7\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0874b66 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0874b66\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1b218ac\" data-id=\"1b218ac\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-09deaa6 elementor-widget elementor-widget-text-editor\" data-id=\"09deaa6\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><b>Series temporales :<\/b><\/p><p><span style=\"font-weight: 400;\">Statsmodels presenta, concretamente, muchas herramientas muy \u00fatiles y completas a la hora de estudiar las series temporales.<\/span><\/p><p><span style=\"font-weight: 400;\">En primer lugar, la <\/span><b>descomposici\u00f3n tendencia-estacionalidad-residuo<\/b><span style=\"font-weight: 400;\">. Si no est\u00e1s familiarizado con estos componentes cl\u00e1sicos de las series temporales, te invitamos a ver nuestro art\u00edculo sobre<\/span> <b>series temporales<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-76f52a3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"76f52a3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3eb11af\" data-id=\"3eb11af\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-90de156 elementor-widget elementor-widget-image\" data-id=\"90de156\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"594\" src=\"https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-2-1024x760.png\" class=\"attachment-large size-large wp-image-40638\" alt=\"\" srcset=\"https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-2-1024x760.png 1024w, https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-2-300x223.png 300w, https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-2-768x570.png 768w, https:\/\/datascientest.com\/es\/files\/2021\/02\/pasted-image-0-2.png 1146w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"> Ejemplo de desglose<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-596d15a elementor-widget elementor-widget-text-editor\" data-id=\"596d15a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Esta funcionalidad permite ver de un vistazo la influencia de los diferentes componentes en nuestro modelo, pero tambi\u00e9n visualizar los residuos para tener una primera idea sobre su <\/span><b>estacionalidad<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Statsmodels tambi\u00e9n implementa dos pruebas estad\u00edsticas para comprobar la estacionalidad de las series temporales : la prueba <strong>Augmented Dickey-Fuller<\/strong> (ADF) y <strong>la prueba KPSS<\/strong> (Kwiatkowski-Phillips-Schmidt-Shin).<\/span><\/p><p><span style=\"font-weight: 400;\">Por \u00faltimo, pero no por ello menos importante, statsmodels ofrece toda una gama de modelos cl\u00e1sicos para<\/span><b> modelar series temporales <\/b><span style=\"font-weight: 400;\">:<\/span><\/p><ul><li><span style=\"font-weight: 400;\">Procesos autorregresivos<\/span><\/li><li><span style=\"font-weight: 400;\">Medias m\u00f3viles<\/span><\/li><li><span style=\"font-weight: 400;\">ARMA y<\/span> <b>ARIMA<\/b><\/li><li><span style=\"font-weight: 400;\">SARIMA, SARIMAX, VARIMAX<\/span><\/li><\/ul><p><span style=\"font-weight: 400;\">SARIMAX es sencillamente un proceso SARIMA que utiliza variables ex\u00f3genas en paralelo para predecir la serie temporal. Mientras que el proceso VARIMAX es un ARIMAX vectorizado que, por tanto, permite predecir m\u00faltiples valores al mismo tiempo.<\/span><\/p><p><span style=\"font-weight: 400;\">Adem\u00e1s de las caracter\u00edsticas presentadas en este art\u00edculo, la biblioteca statsmodels ofrece muchas posibilidades adicionales y sigue creciendo. Para m\u00e1s informaci\u00f3n, se proporciona su documentaci\u00f3n que incluye muchos ejemplos detallados.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e0e58a5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e0e58a5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4a5fd7f\" data-id=\"4a5fd7f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1e47efd elementor-widget elementor-widget-heading\" data-id=\"1e47efd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Conclusi\u00f3n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3c008a4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3c008a4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-86221f5\" data-id=\"86221f5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7fcd9a3 elementor-widget elementor-widget-text-editor\" data-id=\"7fcd9a3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Statsmodels es una herramienta esencial para el estadista o el<\/span><a href=\"https:\/\/datascientest.com\/es\/formacion-data-scientist\"> Data Scientist<\/a><span style=\"font-weight: 400;\">, sobre todo cuando para el estudio de las series temporales. Su sintaxis, f\u00e1cil de entender y reproducible, permite utilizar, modelar y explorar las series estad\u00edsticas sin dificultad.<\/span><\/p><p><span style=\"font-weight: 400;\">Sin embargo, las herramientas que debe dominar un Data Scientist abarcan temas mucho m\u00e1s amplios (Data Visualization, Machine Learning, Deep Learning, etc.) que te invitamos a descubrir en nuestro<\/span><a href=\"https:\/\/datascientest.com\/es\/formacion-data-scientist\"> curso de Data Scientist<\/a><span style=\"font-weight: 400;\">.\u00a0<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eab5f0c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eab5f0c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fde964e\" data-id=\"fde964e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-36595a0 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"36595a0\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/es\/nuestros-cursos-de-data\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\"> Descubrir nuestros cursos de Data<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t<div class=\"elementor-element elementor-element-f09be99 e-flex e-con-boxed e-con\" data-id=\"f09be99\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-0cd82c1 elementor-widget elementor-widget-html\" data-id=\"0cd82c1\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": {\r\n    \"@type\": \"Question\",\r\n    \"name\": \"\u00bfQu\u00e9 hacer con statsmodels?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Statsmodels es una biblioteca de an\u00e1lisis y modelado de datos estad\u00edsticos. Ofrece una serie de funcionalidades que no se encuentran en bibliotecas m\u00e1s tradicionales como sklearn.\"\r\n    }\r\n  }\r\n}\r\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Si alguna vez has intentado modelar series temporales, probablemente habr\u00e1s o\u00eddo hablar de los modelos ARMA o ARIMA. En Python la biblioteca m\u00e1s utilizada para esto es statsmodels. En este art\u00edculo presentaremos r\u00e1pidamente los usos de statsmodels y algunos ejemplos de uso. \u00bfQu\u00e9 hacer con statsmodels? Statsmodels es una biblioteca de an\u00e1lisis y modelado de [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":164811,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2427],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Statsmodels : todo acerca de la biblioteca de Python<\/title>\n<meta name=\"description\" content=\"Si alguna vez has intentado modelar series temporales, probablemente habr\u00e1s o\u00eddo hablar de los modelos ARMA o ARIMA. En Python la biblioteca m\u00e1s utilizada para esto es statsmodels. 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