Series de Tiempo

Concepto

Una serie de tiempo es una colección de observaciones sobre un determinado fenómeno efectuadas en momentos de tiempo sucesivos, usualmente equiespaciados.

Ejemplos de serie de tiempos son: 1.Precio de acciones 2.Niveles de inventario 3.Rotación de personal 4.Ventas

1.Instalar paquetes y llamar librería

library(forecast)
## Warning: package 'forecast' was built under R version 4.1.3
## Registered S3 method overwritten by 'quantmod':
##   method            from
##   as.zoo.data.frame zoo

1.Crear la serie de tiempo

#Paso 1. Obtener los valores dependientes
produccion <- c(50,53,55,57,55,60)
#Paso 2. Agregar a los valores anteriores su tiempo correspondiente
serie_de_tiempo <- ts(data=produccion,start=c(2020,1),frequency=4)
serie_de_tiempo
##      Qtr1 Qtr2 Qtr3 Qtr4
## 2020   50   53   55   57
## 2021   55   60

3. Crear modelo ARIMA

#ARIMA:AutoREgressive Integrarted Moving Average o Modelo Autorregresivo  Integrado de Media Movil
#ARIMA (p,d,q)
#p= orden de auto-regresión
#d= orden de integración (diferenciación)
#q= orden del promedio movil
#¿Cuándo se usa?
#Cuando las estimaciones futuras se explican por los datos del pasado y no porr variables independientes
#Ejemplo: Tipo de cambio
modelo <- auto.arima(serie_de_tiempo, D=1)
modelo
## Series: serie_de_tiempo 
## ARIMA(0,0,0)(0,1,0)[4] with drift 
## 
## Coefficients:
##        drift
##       1.5000
## s.e.  0.1768
## 
## sigma^2 = 2.01:  log likelihood = -2.84
## AIC=9.68   AICc=-2.32   BIC=7.06

4. Realizar el pronóstico

pronostico <- forecast(modelo, level= c(95), h=5)
pronostico
##         Point Forecast    Lo 95    Hi 95
## 2021 Q3             61 58.22127 63.77873
## 2021 Q4             63 60.22127 65.77873
## 2022 Q1             61 58.22127 63.77873
## 2022 Q2             66 63.22127 68.77873
## 2022 Q3             67 63.07028 70.92972
plot (pronostico)

Banco Mundial

0.Concepto

El banco mundial (wb) es un organismo

1.Instalar paquetes y llamar librería

library(forecast)
library(WDI)
## Warning: package 'WDI' was built under R version 4.1.3
library(wbstats)
## Warning: package 'wbstats' was built under R version 4.1.3
library(tidyverse)
## Warning: package 'tidyverse' was built under R version 4.1.3
## Warning: package 'tibble' was built under R version 4.1.3
## Warning: package 'tidyr' was built under R version 4.1.3
## Warning: package 'readr' was built under R version 4.1.3
## Warning: package 'purrr' was built under R version 4.1.3
## Warning: package 'dplyr' was built under R version 4.1.3
## Warning: package 'stringr' was built under R version 4.1.3
## Warning: package 'forcats' was built under R version 4.1.3
## Warning: package 'lubridate' was built under R version 4.1.3
## -- Attaching core tidyverse packages ------------------------ tidyverse 2.0.0 --
## v dplyr     1.1.2     v readr     2.1.4
## v forcats   1.0.0     v stringr   1.5.0
## v ggplot2   3.4.3     v tibble    3.2.1
## v lubridate 1.9.2     v tidyr     1.3.0
## v purrr     1.0.1     
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
## i Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

2. Crear la serie de tiempo

#Paso 1. Obtener los valores dependientes
gdp_data <- wb_data(country = "MX", indicator= "NY.GDP.MKTP.CD", start_date = 1973, end_date = 2022)
gdp_data <-gdp_data %>% select(date,NY.GDP.MKTP.CD)
serie_tiempo1 <- ts(data = gdp_data$NY.GDP.MKTP.CD, start = c(1973, 1), frequency = 1)
serie_tiempo1
## Time Series:
## Start = 1973 
## End = 2022 
## Frequency = 1 
##  [1] 5.528021e+10 7.200018e+10 8.800000e+10 8.887679e+10 8.191250e+10
##  [6] 1.026473e+11 1.345296e+11 2.055770e+11 2.638021e+11 1.846036e+11
## [11] 1.561675e+11 1.842312e+11 1.952414e+11 1.345561e+11 1.475426e+11
## [16] 1.816112e+11 2.214031e+11 2.612537e+11 3.131397e+11 3.631578e+11
## [21] 5.007334e+11 5.278106e+11 3.600725e+11 4.109730e+11 5.004160e+11
## [26] 5.264997e+11 6.002330e+11 7.079099e+11 7.567029e+11 7.721097e+11
## [31] 7.293350e+11 7.822429e+11 8.774769e+11 9.753834e+11 1.052697e+12
## [36] 1.109987e+12 9.000470e+11 1.057801e+12 1.180487e+12 1.201094e+12
## [41] 1.274444e+12 1.315356e+12 1.171870e+12 1.078493e+12 1.158912e+12
## [46] 1.222406e+12 1.269010e+12 1.090515e+12 1.272839e+12 1.414187e+12
## attr(,"label")
## [1] GDP (current US$)
pronostico <-forecast(modelo,level= c(95), h=5)
pronostico
##         Point Forecast    Lo 95    Hi 95
## 2021 Q3             61 58.22127 63.77873
## 2021 Q4             63 60.22127 65.77873
## 2022 Q1             61 58.22127 63.77873
## 2022 Q2             66 63.22127 68.77873
## 2022 Q3             67 63.07028 70.92972
plot(pronostico)

#Paso 2. Agregar a los valores anteriores su tiempo correspondiente
#serie_de_tiempo <- ts(data=produccion, start= c(2020,1), frequency=4)
#serie_de_tiempo

3. Crear modelo ARIMA

#ARIMA:AutoREgressive Integrarted Moving Average o Modelo Autorregresivo  Integrado de Media Movil
#ARIMA (p,d,q)
#p= orden de auto-regresión
#d= orden de integración (diferenciación)
#q= orden del promedio movil
#¿Cuándo se usa?
#Cuando las estimaciones futuras se explican por los datos del pasado y no porr variables independientes
#Ejemplo: Tipo de cambio
modelo1 <- auto.arima(serie_tiempo1, D=1)
modelo1
## Series: serie_tiempo1 
## ARIMA(0,1,0) 
## 
## sigma^2 = 7.381e+21:  log likelihood = -1303.18
## AIC=2608.36   AICc=2608.44   BIC=2610.25
summary(modelo1)
## Series: serie_tiempo1 
## ARIMA(0,1,0) 
## 
## sigma^2 = 7.381e+21:  log likelihood = -1303.18
## AIC=2608.36   AICc=2608.44   BIC=2610.25
## 
## Training set error measures:
##                       ME        RMSE         MAE      MPE     MAPE     MASE
## Training set 27179245230 85046847387 67623045592 4.943879 14.05421 0.980016
##                     ACF1
## Training set -0.01519178

4. Realizar el pronóstico

pronostico1 <-forecast(modelo1,level= c(95), h=5)
pronostico1
##      Point Forecast        Lo 95        Hi 95
## 2023   1.414187e+12 1.245806e+12 1.582568e+12
## 2024   1.414187e+12 1.176060e+12 1.652314e+12
## 2025   1.414187e+12 1.122543e+12 1.705832e+12
## 2026   1.414187e+12 1.077425e+12 1.750949e+12
## 2027   1.414187e+12 1.037676e+12 1.790699e+12
plot (pronostico1)

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