library(forecast)
library(tidyverse)
indice_general <- ts(df$`1 Índice general`, start = c(2009, 12), frequency = 12)

Gráfico de la serie temporal

autoplot(indice_general)+ggtitle("Indice General de Precios al Consumidor")

Análisis de estacionalidad

No se logra identificar una estacionalidad clara en el gráfico de caja por mes.

# Extraer el mes y el año de las fechas
datos <- df %>%
  mutate(mes = format(fecha, "%m"))


# Crear el gráfico de caja
ggplot(datos, aes(x = mes, y = `1 Índice general`)) +
  geom_boxplot() +
  labs(title = "Gráfico de Caja del Indice General IPC por Mes",
       x = "Mes",
       y = "Remesas") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

autoplot(decompose(indice_general))

Modelo utilizando la media

modelo_media=Arima(indice_general, order = c(0,1,0), include.mean = T,include.drift = T)

pronosticos_media=forecast(modelo_media,h=24)

autoplot(pronosticos_media)

checkresiduals(modelo_media)

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(0,1,0) with drift
## Q* = 64.255, df = 23, p-value = 9.06e-06
## 
## Model df: 1.   Total lags used: 24
autoplot(modelo_media)

Pronósticos del modelo utilizando la media

pronosticos_media
##          Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
## Jul 2024       131.2687 130.6673 131.8701 130.3489 132.1885
## Aug 2024       131.4474 130.5968 132.2979 130.1466 132.7481
## Sep 2024       131.6260 130.5843 132.6677 130.0329 133.2192
## Oct 2024       131.8047 130.6019 133.0076 129.9651 133.6443
## Nov 2024       131.9834 130.6386 133.3282 129.9267 134.0401
## Dec 2024       132.1621 130.6889 133.6352 129.9090 134.4151
## Jan 2025       132.3407 130.7495 133.9320 129.9072 134.7743
## Feb 2025       132.5194 130.8184 134.2205 129.9179 135.1210
## Mar 2025       132.6981 130.8938 134.5024 129.9387 135.4575
## Apr 2025       132.8768 130.9749 134.7786 129.9681 135.7854
## May 2025       133.0555 131.0608 135.0501 130.0049 136.1061
## Jun 2025       133.2341 131.1508 135.3175 130.0479 136.4204
## Jul 2025       133.4128 131.2444 135.5813 130.0965 136.7292
## Aug 2025       133.5915 131.3412 135.8418 130.1499 137.0330
## Sep 2025       133.7702 131.4409 136.0995 130.2078 137.3325
## Oct 2025       133.9489 131.5432 136.3545 130.2697 137.6280
## Nov 2025       134.1275 131.6478 136.6072 130.3351 137.9199
## Dec 2025       134.3062 131.7546 136.8578 130.4039 138.2086
## Jan 2026       134.4849 131.8634 137.1064 130.4756 138.4942
## Feb 2026       134.6636 131.9739 137.3532 130.5501 138.7770
## Mar 2026       134.8422 132.0862 137.5983 130.6272 139.0573
## Apr 2026       135.0209 132.2000 137.8418 130.7067 139.3351
## May 2026       135.1996 132.3153 138.0839 130.7884 139.6108
## Jun 2026       135.3783 132.4319 138.3246 130.8722 139.8843

Modelo utilizando ARIMA

modelo_arima1=Arima(indice_general, order = c(1,1,3))

pronosticos_arima1=forecast(modelo_arima1,h=24)

autoplot(pronosticos_arima1)

checkresiduals(modelo_arima1)

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(1,1,3)
## Q* = 27.205, df = 20, p-value = 0.1296
## 
## Model df: 4.   Total lags used: 24
autoplot(modelo_arima1)

Pronósticos utilizando ARIMA(1,1,3)

pronosticos_arima1
##          Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
## Jul 2024       131.2860 130.7174 131.8546 130.4164 132.1556
## Aug 2024       131.4087 130.4681 132.3493 129.9701 132.8472
## Sep 2024       131.5606 130.3571 132.7640 129.7201 133.4011
## Oct 2024       131.7053 130.2444 133.1661 129.4711 133.9395
## Nov 2024       131.8431 130.1263 133.5599 129.2174 134.4688
## Dec 2024       131.9744 130.0014 133.9474 128.9570 134.9918
## Jan 2025       132.0995 129.8696 134.3294 128.6892 135.5098
## Feb 2025       132.2186 129.7309 134.7063 128.4140 136.0232
## Mar 2025       132.3321 129.5857 135.0785 128.1319 136.5324
## Apr 2025       132.4402 129.4345 135.4460 127.8433 137.0372
## May 2025       132.5432 129.2776 135.8088 127.5489 137.5376
## Jun 2025       132.6413 129.1156 136.1671 127.2492 138.0335
## Jul 2025       132.7348 128.9489 136.5206 126.9448 138.5247
## Aug 2025       132.8238 128.7780 136.8696 126.6363 139.0113
## Sep 2025       132.9086 128.6033 137.2139 126.3243 139.4929
## Oct 2025       132.9894 128.4252 137.5535 126.0091 139.9697
## Nov 2025       133.0663 128.2440 137.8886 125.6913 140.4414
## Dec 2025       133.1396 128.0602 138.2191 125.3713 140.9080
## Jan 2026       133.2094 127.8739 138.5450 125.0494 141.3694
## Feb 2026       133.2760 127.6856 138.8664 124.7262 141.8257
## Mar 2026       133.3393 127.4954 139.1832 124.4018 142.2768
## Apr 2026       133.3997 127.3036 139.4957 124.0766 142.7227
## May 2026       133.4572 127.1106 139.8038 123.7509 143.1634
## Jun 2026       133.5119 126.9164 140.1075 123.4249 143.5989

Modelo utilizando auto.arima

modelo_autoarima=auto.arima(indice_general, stepwise = F)

pronosticos_autoarima= forecast(modelo_autoarima)

autoplot(pronosticos_autoarima)

checkresiduals(modelo_autoarima)

## 
##  Ljung-Box test
## 
## data:  Residuals from ARIMA(1,1,0)(2,0,0)[12] with drift
## Q* = 28.851, df = 20, p-value = 0.09073
## 
## Model df: 4.   Total lags used: 24
autoplot(modelo_autoarima)

Pronósticos utilizando auto.arima

pronosticos_autoarima
##          Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
## Jul 2024       131.2729 130.7116 131.8343 130.4144 132.1315
## Aug 2024       131.4445 130.5129 132.3762 130.0197 132.8694
## Sep 2024       131.6228 130.3931 132.8525 129.7421 133.5035
## Oct 2024       131.7648 130.2857 133.2438 129.5028 134.0268
## Nov 2024       131.8437 130.1486 133.5387 129.2513 134.4361
## Dec 2024       131.7704 129.8830 133.6578 128.8839 134.6570
## Jan 2025       131.9619 129.8997 134.0240 128.8081 135.1156
## Feb 2025       132.0685 129.8452 134.2917 128.6683 135.4686
## Mar 2025       132.2319 129.8584 134.6054 128.6019 135.8618
## Apr 2025       132.4901 129.9753 135.0048 128.6441 136.3361
## May 2025       132.7213 130.0728 135.3698 128.6707 136.7719
## Jun 2025       132.9237 130.1478 135.6995 128.6784 137.1690
## Jul 2025       133.1267 130.1914 136.0620 128.6375 137.6158
## Aug 2025       133.2875 130.1884 136.3867 128.5478 138.0272
## Sep 2025       133.4555 130.1968 136.7142 128.4717 138.4393
## Oct 2025       133.5816 130.1695 136.9937 128.3632 138.7999
## Nov 2025       133.7348 130.1756 137.2941 128.2914 139.1782
## Dec 2025       134.0188 130.3182 137.7195 128.3591 139.6785
## Jan 2026       134.1628 130.3259 137.9998 128.2947 140.0309
## Feb 2026       134.3137 130.3451 138.2822 128.2443 140.3830
## Mar 2026       134.4996 130.4037 138.5955 128.2355 140.7638
## Apr 2026       134.6653 130.4459 138.8848 128.2123 141.1184
## May 2026       134.8407 130.5013 139.1802 128.2041 141.4773
## Jun 2026       134.9928 130.5365 139.4490 128.1776 141.8080