####################
#                  #
#    Exercise 1    #
#                  #
####################
auto <- read.csv("D:/forecasting-tutorial/vehicle.csv")
plot(auto$sales,
     type = "n",
     ylim = c(0, 5000),
     ylab = "Sales, '000 units",
     xlab = "Period",
     main = "US light vehicle sales in 1976-2016")
lines(auto$sales)
#plot of chunk forecasting-part-4
####################
#                  #
#    Exercise 2    #
#                  #
####################
auto$trend <- seq(1:nrow(auto))
auto$income_lag <- c(NA, auto$income[1:nrow(auto)-1])
auto$unemp_lag <- c(NA, auto$unemp[1:nrow(auto)-1])
auto$rate_lag <- c(NA, auto$rate[1:nrow(auto)-1])
####################
#                  #
#    Exercise 3    #
#                  #
####################
regressions_result <- regsubsets(sales ~ ., data = auto)
plot(regressions_result, col = colorRampPalette(c("darkgreen", "grey"))(10))
plot(regressions_result, col = colorRampPalette(c("darkgreen", "grey"))(10))
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