Actividad serie de tiempo

library(dplyr)

## 
## Adjuntando el paquete: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(forecast)

library(readxl)
datos <- read_excel("C:/Users/Alberto JIménez/Downloads/Ventas_Históricas_Lechitas.xlsx", skip = 2)

## New names:
## • `Mes` -> `Mes...1`
## • `Ventas` -> `Ventas...2`
## • `Mes` -> `Mes...3`
## • `Ventas` -> `Ventas...4`
## • `Mes` -> `Mes...5`
## • `Ventas` -> `Ventas...6`

datos <- na.omit(datos)

datos_unificados <- bind_rows(
  datos %>% select(Mes = Mes...1, Ventas = Ventas...2),
  datos %>% select(Mes = Mes...3, Ventas = Ventas...4),
  datos %>% select(Mes = Mes...5, Ventas = Ventas...6)
)

datos_unificados$Mes <- as.Date(as.numeric(datos_unificados$Mes), origin = "1899-12-30")

# 1 Tendencia Secular

serie_de_tiempo <- ts(
  data = datos_unificados$Ventas,
  start = c(2018, 1),
  frequency = 12
)

modelo <- auto.arima(serie_de_tiempo)
summary(modelo)

## Series: serie_de_tiempo 
## ARIMA(1,0,0)(1,1,0)[12] with drift 
## 
## Coefficients:
##          ar1     sar1     drift
##       0.6383  -0.5517  288.8979
## s.e.  0.1551   0.2047   14.5026
## 
## sigma^2 = 202701:  log likelihood = -181.5
## AIC=371   AICc=373.11   BIC=375.72
## 
## Training set error measures:
##                    ME    RMSE    MAE        MPE      MAPE       MASE      ACF1
## Training set 25.22158 343.864 227.17 0.08059932 0.7069542 0.06491044 0.2081026

pronostico <- forecast(
  modelo,
  level = 95,
  h = 12   # 12 meses = 1 año
)

pronostico

##          Point Forecast    Lo 95    Hi 95
## Jan 2021       35498.90 34616.48 36381.32
## Feb 2021       34202.17 33155.28 35249.05
## Mar 2021       36703.01 35596.10 37809.92
## Apr 2021       36271.90 35141.44 37402.36
## May 2021       37121.98 35982.07 38261.90
## Jun 2021       37102.65 35958.90 38246.40
## Jul 2021       37151.04 36005.73 38296.34
## Aug 2021       38564.64 37418.70 39710.58
## Sep 2021       38755.22 37609.03 39901.42
## Oct 2021       39779.02 38632.72 40925.32
## Nov 2021       38741.63 37595.28 39887.97
## Dec 2021       38645.86 37499.50 39792.22

plot(pronostico)