{"id":3030,"date":"2021-10-21T16:12:56","date_gmt":"2021-10-21T16:12:56","guid":{"rendered":"https:\/\/ricovictor.com\/?p=3030"},"modified":"2026-01-29T21:45:06","modified_gmt":"2026-01-29T21:45:06","slug":"prediccion-con-modelos-autorregresivos-ar-en-r","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2021\/10\/21\/prediccion-con-modelos-autorregresivos-ar-en-r\/","title":{"rendered":"Predicci\u00f3n con Modelos Autorregresivos (AR) en R"},"content":{"rendered":"\t\t
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En este video vamos a ver como podemos realizar una Predicci\u00f3n con Modelos Autorregresivos(AR) usando R. <\/strong><\/p>

Dentro de la estad\u00edstica, un modelo autorregresivo (AR) es una representaci\u00f3n de un proceso aleatorio(estoc\u00e1stico), en el que la variable de inter\u00e9s depende de sus observaciones pasadas.<\/p>

Espec\u00edficamente, la variable de inter\u00e9s o de salida, depende linealmente de sus valores anteriores. Este tutorial es continuaci\u00f3n del siguiente video que ya realic\u00e9 hace unas semanas. Puedes revisarlo si no sabes de que va el tema.<\/p>

M\u00e1s abajo tienes el C\u00d3DIGO EN R usado en el video<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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C\u00d3DIGO EN R<\/strong><\/em><\/p>

\u00a0<\/p>

#video Proceso Autoregresivo (AR)<\/strong><\/p>

#install.packages(\u00abquantmod\u00bb)
library(quantmod)
#install.packages(\u00abtseries\u00bb)
library(tseries)
#install.packages(\u00abforecast\u00bb)
library(forecast)
#install.packages(\u00abTSA\u00bb)
library(TSA)
#install.packages(\u00abQuandl\u00bb)
library(Quandl)
library(stats)<\/p>

#getSymbols(\u00abCPIAUCNS\u00bb,src=\u00bbFRED\u00bb)<\/p>

getSymbols(\u00abEXUSEU\u00bb,src=\u00bbFRED\u00bb)<\/p>

plot(EXUSEU,type=\u00bbl\u00bb)<\/p>

#Modelo AR(1)<\/p>

MAR_100= Arima(EXUSEU,order=c(1,0,0))
MAR_100<\/p>

residuos <- MAR_100$residuals<\/p>

plot(residuos,type=\u00bbl\u00bb)<\/p>

summary(MAR_100)<\/p>

#adf.test(EXUSEU)<\/p>

ar1 <- arima(EXUSEU,order=c(2,0,0))
ar1<\/p>

# PREDICCI\u00d3N CON MODELO AR(AUTOREGRESIVOS)<\/p>

EURUSD_ts <- ts( EXUSEU$EXUSEU)
val_fijados <- EURUSD_ts – residuos<\/p>

plot(EURUSD_ts,type=\u00bbl\u00bb)
lines(val_fijados,type=\u00bbl\u00bb,col=\u00bbred\u00bb)<\/p>

# Pasos<\/p>

#1 FIJAR EL MODELO
#2 REALIZAMOS LA PREDICCI\u00d3N — predict
#3 A\u00d1ADIMOS UN INTERVALO DE CONFIANZA<\/p>

#1<\/p>

MAR_100= Arima(EXUSEU,order=c(1,0,0))
MAR_100<\/p>

residuos <- MAR_100$residuals<\/p>

plot(residuos,type=\u00bbl\u00bb)<\/p>

summary(MAR_100)<\/p>

#2 PREDICT<\/p>

MAR_predict <- predict(MAR_100, n.ahead = 10)
#Modelo AR, num pred
MAR_predict<\/p>

MAR_prediccion <- MAR_predict$pred
plot(MAR_prediccion,type=\u00bbl\u00bb)<\/p>

MAR_error <- MAR_predict$se
plot(MAR_error,type=\u00bbl\u00bb)<\/p>

plot.ts(EURUSD_ts,xlim = c(0, 300),type=\u00bbl\u00bb)<\/p>

points(MAR_prediccion , type = \u00abl\u00bb, col = \u00abblue\u00bb)<\/p>

#3 intevalos confianza<\/p>

int_conf <- data.frame(MAR_prediccion-MAR_error,MAR_prediccion,MAR_prediccion+MAR_error)<\/p>

int_conf_2 <- data.frame(MAR_prediccion-(MAR_error*2),MAR_prediccion,MAR_prediccion+(MAR_error*2))<\/p>

plot.ts(EURUSD_ts,xlim = c(0, 300),type=\u00bbl\u00bb)<\/p>

points(MAR_prediccion , type = \u00abl\u00bb, col = \u00abblue\u00bb)
points(MAR_prediccion – MAR_error*2, type = \u00abl\u00bb, col = \u00abred\u00bb, lty = 2)
points(MAR_prediccion + MAR_error*2, type = \u00abl\u00bb, col = \u00abred\u00bb, lty = 2)<\/p>

points(MAR_prediccion – MAR_error, type = \u00abl\u00bb, col = \u00abgreen\u00bb, lty = 2)
points(MAR_prediccion + MAR_error, type = \u00abl\u00bb, col = \u00abgreen\u00bb, lty = 2)<\/p>

# R2 =1- SCE\/SCT<\/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\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

En este video vamos a ver como podemos realizar una Predicci\u00f3n con Modelos Autorregresivos(AR) usando R. Dentro de la estad\u00edstica, un modelo autorregresivo (AR) es una representaci\u00f3n de un proceso aleatorio(estoc\u00e1stico), en el que la variable de inter\u00e9s depende de sus observaciones pasadas. Espec\u00edficamente, la variable de inter\u00e9s o de salida, depende linealmente de sus …<\/p>\n

Predicci\u00f3n con Modelos Autorregresivos (AR) en R<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":19,"featured_media":3035,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_canvas","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":"","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":[2],"tags":[],"yoast_head":"\nPredicci\u00f3n con Modelos Autorregresivos (AR) en R - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas<\/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:\/\/ricovictor.com\/index.php\/2021\/10\/21\/prediccion-con-modelos-autorregresivos-ar-en-r\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predicci\u00f3n con Modelos Autorregresivos (AR) en R - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas\" \/>\n<meta property=\"og:description\" content=\"En este video vamos a ver como podemos realizar una Predicci\u00f3n con Modelos Autorregresivos(AR) usando R. Dentro de la estad\u00edstica, un modelo autorregresivo (AR) es una representaci\u00f3n de un proceso aleatorio(estoc\u00e1stico), en el que la variable de inter\u00e9s depende de sus observaciones pasadas. 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