{"id":3864,"date":"2024-01-04T19:28:58","date_gmt":"2024-01-04T19:28:58","guid":{"rendered":"https:\/\/ricovictor.com\/?p=3864"},"modified":"2024-01-04T19:44:33","modified_gmt":"2024-01-04T19:44:33","slug":"modelos-sarimaarima-estacionales-que-son-y-como-usarlos-para-predecir","status":"publish","type":"post","link":"https:\/\/ricovictor.com\/index.php\/2024\/01\/04\/modelos-sarimaarima-estacionales-que-son-y-como-usarlos-para-predecir\/","title":{"rendered":"Modelos SARIMA(Arima Estacionales).\u00bfQu\u00e9 son y c\u00f3mo usarlos para Predecir?"},"content":{"rendered":"\t\t
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\u00a0<\/p>

Los Modelos SARIMA<\/strong>, tambi\u00e9n llamados Arima Estacionales<\/strong>, son un tipo de modelos econom\u00e9tricos que se usan para buscar patrones en las series temporales y poder hacer predicciones.<\/p>

Son una variante de los Modelos ARIMA cuya peculiaridad es que tiene en cuenta las estacionalidad de las series temporales para crear el modelo. En este video tienes un ejemplo de las funciones que puedes usar en R para crear este tipo de Modelos de manera autom\u00e1tica.<\/p>

Abajo tienes el c\u00f3digo en R usado en el video<\/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

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CODIGO R DEL VIDEO<\/span><\/em><\/p>


## PROCESOS ARIMA Y SARIMA<\/em><\/p>

#Nuestro objetivo ser\u00e1 estudiar la evoluci\u00f3n de una serie de tiempo<\/em>
#buscando ser capaz de modelizar y obtener predicciones<\/em><\/p>

#ARIMA <- modelo estad\u00edstico que utiliza variaciones y <\/em>
#regresiones de datos estad\u00edsticos con el fin de encontrar <\/em>
#patrones para una predicci\u00f3n hacia el futuro. <\/em>
#Se trata de un modelo din\u00e1mico de series temporales, es decir, <\/em>
#las estimaciones futuras vienen explicadas por los datos del pasado <\/em>
#y no por variables independientes.<\/em><\/p>

# ARIMA(p,d,q) Compuesto por 3 par\u00e1metros (Parte o Retardos Regulares)<\/em><\/p>

# SARIMA(p,d.q),(P,D,Q), periodo= \u00abx\u00bb (Parte estacional)<\/em><\/p>

## Los modelos SARIMA tienen los momentos y correlogramas iguales<\/em>
# que los ARIMA pero en vez de considerar los retardos regulares<\/em>
## nos fijaremos en los retardos estacionales<\/em><\/p>

\u00a0<\/p>

### Vamos a obtener el mejor Arima Estacional<\/em><\/p>

### Diremos que una serie es estacional cuando su media no es constante en<\/em>
### en el tiempo pero var\u00eda de forma peri\u00f3dica, ciclicamente E(Xt) = E(Xt+s)<\/em><\/p>

# En serie diarias con estacionalidad Semanal <- S = 7<\/em>
# En series Mensuales con estacionalidad Anual <- S = 12<\/em>
# En series Horarias con estacionalidad diaria <- S= 24<\/em>
# En series Bimensuales la estacionalidad anual es <- S= 6 y <\/em>
# analogamente con las Cuatrimestrales <- S = 3 y las trimestrales S=4<\/em><\/p>

\u00a0<\/p>

### Dos formas de obtener la Estacionalidad en Arima<\/em><\/p>


## 1. Periodrama + funciones de autocorrelacion para el modelo<\/em><\/p>

## #Estacionalidad<\/em>
#La realizamos llevando a cabo un an\u00e1lisis espectral, toda serie temporal aleatoria se puede representar mediante una combinaci\u00f3n de ondas seno(x) o coseno (x) siendo x la serie temporal. Dichas ondas difieren en el tiempo que tardan en completarse.<\/em>
#No nos vamos a extender en el c\u00e1lculo debido a que se usa un c\u00e1lculo complejo (Transformada de Fourier).<\/em><\/p>

## 2. Fuerza bruta + AIC<\/em><\/p>

\u00a0<\/p>


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


getSymbols(\u00abCPIAUCNS\u00bb,src=\u00bbFRED\u00bb) #IPC consumidores urbanos<\/em><\/p>


#getSymbols(\u00abUNRATE\u00bb,src=\u00bbFRED\u00bb) #tasa de desmepleo EEUU<\/em>
#getSymbols(\u00abGS10″,src=\u00bbFRED\u00bb) #letras del tesoro a 10 a\u00f1s<\/em>
#getSymbols(\u00abFEDFUNDS\u00bb,src=\u00bbFRED\u00bb) #tasa interes fondos federales<\/em>
#getSymbols(\u00abEXUSEU\u00bb,src=\u00bbFRED\u00bb) #EURSUD mensual<\/em>
#getSymbols(\u00abCCRETT01USM661N\u00bb,src=\u00bbFRED\u00bb) #Tipo de cambio efectivo IPC<\/em>
#getSymbols(\u00abM2NS\u00bb,src=\u00bbFRED\u00bb) #Stock de dinero M2<\/em>
#getSymbols(\u00abPAYEMS\u00bb,src=\u00bbFRED\u00bb) #trabajadores no rurales<\/em>
#getSymbols(\u00abBOPGSTB\u00bb,src=\u00bbFRED\u00bb) #Balance cuenta, Diferencia entre exp e imp<\/em><\/p>


IPC=CPIAUCNS[\u00ab2001-01::2019-12\u00bb]<\/em><\/p>

#desempleo=UNRATE[\u00ab2001-01::2019-12\u00bb]<\/em>
#bono10=GS10[\u00ab2001-01::2019-12\u00bb]<\/em>
#Fedfunds=FEDFUNDS[\u00ab2001-01::2019-12\u00bb]<\/em>
#EUR=EXUSEU[\u00ab2001-01::2019-12\u00bb]<\/em>
#credit=CCRETT01USM661N[\u00ab2001-01::2019-12\u00bb]<\/em>
#pay=PAYEMS[\u00ab2001-01::2019-12\u00bb]<\/em>
#money=M2NS[\u00ab2001-01::2019-12\u00bb]<\/em>
#trade=BOPGSTB[\u00ab2001-01::2019-12\u00bb]<\/em><\/p>

\u00a0<\/p>

\u00a0<\/p>


#IPC<\/em><\/p>

y= IPC$CPIAUCNS<\/em><\/p>

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

adf.test(y,alternative=\u00bbstationary\u00bb,k=0)<\/em><\/p>

#vemos que el p-value es mayor a 0.05<\/em><\/p>


#Por el grafico vemos como hay una tendencia. Para estar seguros<\/em>
#probamos el test de autocorrelacion<\/em><\/p>

acf(y)<\/em><\/p>


# -2.003<\/em><\/p>

dy=na.omit(diff(y)) # Diferenciamos para sacar una serie estacionaria<\/em>
adf.test(dy,alternative=\u00bbstationary\u00bb,k=0)<\/em><\/p>

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

\u00a0<\/p>

## Usar periodograma<\/em><\/p>

p=periodogram(dy)<\/em>
max(p$spec)<\/em>
p$freq[match(max(p$spec),p$spec)]<\/em>
1\/p$freq[match(max(p$spec),p$spec)]<\/em>
# 12<\/em><\/p>


## Eleccion del modelo con autocorrelacion<\/em><\/p>

#ahora calcularemos los test de correlacion y correlacion parcial para ver que <\/em>
#que modelos utilizaremos para intentar predecir la serie<\/em><\/p>

acf(dy)<\/em>
pacf(dy)<\/em><\/p>

\u00a0<\/p>


##FUNCION AUTOARIMA + PRUEBA DE MODELOS<\/em><\/p>


# NO he podido hacer autoarima estacional<\/em><\/p>

Modelo1=auto.arima(y) <\/em>
Modelo1 #arima 2,1,4<\/em><\/p>


Ms010= Arima(y,order=c(2,1,4),seasonal=list(order=c(0,1,0),period=12))<\/em>
Ms010<\/em>
#537.85<\/em><\/p>

ms111= Arima(y,order=c(2,1,4),seasonal=list(order=c(1,1,1),period=12))<\/em>
ms111<\/em>
#409.01<\/em><\/p>

ms212= Arima(y,order=c(2,1,4),seasonal=list(order=c(2,1,2),period=12))<\/em>
ms212<\/em>
#410.07<\/em><\/p>

\u00a0<\/p>

# Modelo final<\/em><\/p>

#Parte1 con autoarima<\/em>
#parte 2 probando con varios modelos y eligiendo el menor AIC<\/em>
#parte 3 — hecho al principio con la estacionalidad<\/em><\/p>

MSAR= Arima(y,order=c(2,1,4),seasonal=list(order=c(1,1,1),period=12))<\/em>
MSAR<\/em>
#Arima()<\/em><\/p>

pred2 <- forecast(MSAR, h=10)<\/em>
plot(pred2,xlab=\u00bbIPC\u00bb,col=\u00bbred\u00bb)<\/em>
Prediccion2<- pred2$mean<\/em>
Prediccion2<\/em>
PredYERROR_2=data.frame(predict(MSAR,n.ahead=3))<\/em>
PredYERROR_2<\/em><\/p>

\u00a0<\/p>

\u00a0<\/p>

\u00a0<\/p>

## ALGORITMO FUERZA BRUTA<\/em><\/p>

FuerzaUniversal = function( datos, valores ){ <\/em>

#Parallel<\/em>
require(doParallel)<\/em>
no_cores <- (detectCores() – 1)<\/em>
cl<- makeCluster(no_cores)<\/em>
registerDoParallel(cl) <\/em>

y= datos <\/em>


primeraVuelta = FALSE<\/em>

primeraVuelta = FALSE<\/em>
for(order_x in 0:2){<\/em>
for(order_y in 0:2){<\/em>
for(order_z in 0:2){<\/em>
for(periodo in 12:12){<\/em>

for(seasonal_x in 0:2){<\/em>
for(seasonal_y in 0:2){<\/em>
for(seasonal_z in 0:2){<\/em>
actual = tryCatch(arima(datos,order=c(order_x,order_y,order_z),seasonal=list(order=c(seasonal_x,seasonal_y,seasonal_z),period=periodo)),<\/em>
error =function(err)FALSE)<\/em>

if( !is.logical(actual)){<\/em>
print(c(order_x,order_y,order_z,seasonal_x,seasonal_y,seasonal_z,periodo, actual$aic))<\/em>
if(primeraVuelta == FALSE){<\/em>
primeraVuelta = TRUE<\/em>
menor = actual$aic<\/em>

aOK = order_x<\/em>
bOK = order_y<\/em>
cOK = order_z<\/em>

xOk = seasonal_x<\/em>
yOk = seasonal_y<\/em>
zOk = seasonal_z<\/em>
period = periodo<\/em>
}<\/em>
if (actual$aic < menor){<\/em>
menor = actual$aic<\/em>
aOK = order_x<\/em>
bOK = order_y<\/em>
cOK = order_z<\/em>


xOk = seasonal_x<\/em>
yOk = seasonal_y<\/em>
zOk = seasonal_z<\/em>
period = periodo<\/em>
} <\/em>
}<\/em>
}<\/em>
}<\/em>
} <\/em>
}<\/em>
}<\/em>
}<\/em>
} <\/em>
stopCluster(cl)<\/em>
coordenadas = c(aOK,bOK,cOK,xOk,yOk,zOk,periodo, menor)<\/em>
}<\/em><\/p>

## POnemos la serie a la que se lo queremos aplicar<\/em>
y= y<\/em><\/p>

goku = FuerzaUniversal(y)<\/em><\/p>


goku<\/em><\/p>

#ModeloFinalSARIMA <- (0,1,1)(1,1,2) (12)<\/em><\/p>

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

\u00a0 Los Modelos SARIMA, tambi\u00e9n llamados Arima Estacionales, son un tipo de modelos econom\u00e9tricos que se usan para buscar patrones en las series temporales y poder hacer predicciones. Son una variante de los Modelos ARIMA cuya peculiaridad es que tiene en cuenta las estacionalidad de las series temporales para crear el modelo. En este video …<\/p>\n

Modelos SARIMA(Arima Estacionales).\u00bfQu\u00e9 son y c\u00f3mo usarlos para Predecir?<\/span> Leer m\u00e1s »<\/a><\/p>\n","protected":false},"author":19,"featured_media":3866,"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":"","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":"\nModelos SARIMA(Arima Estacionales).\u00bfQu\u00e9 son y c\u00f3mo usarlos para Predecir? - 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\/2024\/01\/04\/modelos-sarimaarima-estacionales-que-son-y-como-usarlos-para-predecir\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Modelos SARIMA(Arima Estacionales).\u00bfQu\u00e9 son y c\u00f3mo usarlos para Predecir? - V\u00edctor A. Rico \u2014 Econometr\u00eda y Finanzas Cuantitativas\" \/>\n<meta property=\"og:description\" content=\"\u00a0 Los Modelos SARIMA, tambi\u00e9n llamados Arima Estacionales, son un tipo de modelos econom\u00e9tricos que se usan para buscar patrones en las series temporales y poder hacer predicciones. Son una variante de los Modelos ARIMA cuya peculiaridad es que tiene en cuenta las estacionalidad de las series temporales para crear el modelo. En este video … Modelos SARIMA(Arima Estacionales).\u00bfQu\u00e9 son y c\u00f3mo usarlos para Predecir? Leer m\u00e1s »\" \/>\n<meta property=\"og:url\" content=\"https:\/\/ricovictor.com\/index.php\/2024\/01\/04\/modelos-sarimaarima-estacionales-que-son-y-como-usarlos-para-predecir\/\" \/>\n<meta property=\"og:site_name\" content=\"V\u00edctor A. 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