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)
