library(forecast)

leche <- c(
25520.51,23740.11,26253.58,25868.43,27072.87,27150.50,27067.10,28145.25,27546.29,28400.37,27441.98,27852.47,
28463.69,26996.11,29768.20,29292.51,29950.68,30099.17,30851.26,32271.76,31940.74,32995.93,32197.12,31984.82,
32496.44,31287.28,33376.02,32949.77,34004.11,33757.89,32927.30,34324.12,35151.28,36133.07,34799.91,34846.17
)

serie_leche <- ts(data = leche,
                  start = c(2017,1),
                  frequency = 12)

serie_leche

##           Jan      Feb      Mar      Apr      May      Jun      Jul      Aug
## 2017 25520.51 23740.11 26253.58 25868.43 27072.87 27150.50 27067.10 28145.25
## 2018 28463.69 26996.11 29768.20 29292.51 29950.68 30099.17 30851.26 32271.76
## 2019 32496.44 31287.28 33376.02 32949.77 34004.11 33757.89 32927.30 34324.12
##           Sep      Oct      Nov      Dec
## 2017 27546.29 28400.37 27441.98 27852.47
## 2018 31940.74 32995.93 32197.12 31984.82
## 2019 35151.28 36133.07 34799.91 34846.17

modelo <- auto.arima(serie_leche)
modelo       

## Series: serie_leche 
## ARIMA(1,0,0)(1,1,0)[12] with drift 
## 
## Coefficients:
##          ar1     sar1     drift
##       0.6383  -0.5517  288.8980
## s.e.  0.1551   0.2047   14.5025
## 
## sigma^2 = 202700:  log likelihood = -181.5
## AIC=371   AICc=373.11   BIC=375.72

summary(modelo)

## Series: serie_leche 
## ARIMA(1,0,0)(1,1,0)[12] with drift 
## 
## Coefficients:
##          ar1     sar1     drift
##       0.6383  -0.5517  288.8980
## s.e.  0.1551   0.2047   14.5025
## 
## sigma^2 = 202700:  log likelihood = -181.5
## AIC=371   AICc=373.11   BIC=375.72
## 
## Training set error measures:
##                    ME    RMSE      MAE        MPE      MAPE       MASE
## Training set 25.22163 343.863 227.1699 0.08059942 0.7069541 0.06491041
##                   ACF1
## Training set 0.2081043

pronostico <- forecast(modelo, level = c(95), h = 12)
pronostico

##          Point Forecast    Lo 95    Hi 95
## Jan 2020       35498.90 34616.48 36381.32
## Feb 2020       34202.17 33155.29 35249.05
## Mar 2020       36703.01 35596.10 37809.92
## Apr 2020       36271.90 35141.44 37402.36
## May 2020       37121.98 35982.07 38261.90
## Jun 2020       37102.65 35958.91 38246.40
## Jul 2020       37151.04 36005.74 38296.35
## Aug 2020       38564.65 37418.71 39710.59
## Sep 2020       38755.23 37609.03 39901.42
## Oct 2020       39779.03 38632.73 40925.33
## Nov 2020       38741.63 37595.29 39887.97
## Dec 2020       38645.86 37499.50 39792.22

plot(pronostico)