Lab Session 8
retaildata <- readxl::read_excel("retail.xlsx", skip = 1)
mytimeseries <- ts(retaildata[["A3349873A"]],
frequency=12, start=c(1982,4))
fit <- ets(mytimeseries)
summary(fit)
## ETS(M,A,M)
##
## Call:
## ets(y = mytimeseries)
##
## Smoothing parameters:
## alpha = 0.5067
## beta = 1e-04
## gamma = 0.3222
##
## Initial states:
## l = 63.0202
## b = 0.7934
## s=0.9391 0.912 0.9294 1.5281 1.0577 0.9868
## 0.9604 0.941 0.9431 0.901 0.9661 0.9353
##
## sigma: 0.0488
##
## AIC AICc BIC
## 4018.808 4020.494 4085.835
##
## Training set error measures:
## ME RMSE MAE MPE MAPE MASE
## Training set 0.1576127 13.5932 8.70378 -0.09176148 3.711149 0.4596804
## ACF1
## Training set 0.008030422
autoplot(forecast(fit))

x1 <- window(mytimeseries, end=c(2010,12))
x2 <- window(mytimeseries, start=2011)
f1 <- snaive(x1, h=length(x2))
f2 <- hw(x1, h=length(x2), seasonal='multi')
f3 <- forecast(ets(x1), h=length(x2))
accuracy(f1,x2)
## ME RMSE MAE MPE MAPE MASE
## Training set 7.772973 20.24576 15.95676 4.702754 8.109777 1.000000
## Test set 81.744444 100.00869 82.06667 20.549055 20.669738 5.143067
## ACF1 Theil's U
## Training set 0.7385090 NA
## Test set 0.6830879 1.67023
accuracy(f2,x2)
## ME RMSE MAE MPE MAPE MASE
## Training set 0.2335309 8.817788 6.393003 0.1048658 3.20614 0.4006455
## Test set 78.8187005 95.779065 78.818701 20.0382885 20.03829 4.9395188
## ACF1 Theil's U
## Training set 0.06611033 NA
## Test set 0.52771581 1.630125
accuracy(f3,x2)
## ME RMSE MAE MPE MAPE
## Training set 0.3404863 8.784116 6.289258 0.1876561 3.128791
## Test set 95.8058083 113.202964 95.805808 24.5580948 24.558095
## MASE ACF1 Theil's U
## Training set 0.3941439 0.01203982 NA
## Test set 6.0040903 0.61158849 1.922081
bicoal %>% ets %>% forecast %>% autoplot

chicken %>% ets %>% forecast %>% autoplot

dole %>% ets %>% forecast %>% autoplot

usdeaths %>% ets %>% forecast %>% autoplot
