1. Identify, estimate and generate forecasts from ARIMA models for the usmelec, leisure and h02 as specified below:

   usmelec <- as_tsibble(fpp2::usmelec) |>
     rename(Month = index, Generation = value)
   
   leisure <- us_employment |>
     filter(Title == "Leisure and Hospitality", year(Month) > 2000) |>
     mutate(Employed = Employed/1000) |> select(Month, Employed)
   
   h02 <- PBS |>
     filter(ATC2 == "H02") |>
     summarise(Cost = sum(Cost))

2. Identify, estimate and generate forecasts from ARIMA and ETS models for the aus_economy and cement as specified below:

   aus_economy <- global_economy |> filter(Code == "AUS") |>
     mutate(Population = Population/1e6)
   
   cement <- aus_production |>
     select(Cement) |>
     filter_index("1988 Q1" ~ .)

3. Exam 2024

   • Section A: Q5
   • Section B: Q3 (g-h)
   • Section D: all questions