Week 9: ARIMA models

Tutorial exercises

What you will learn this week

  • Seasonal ARIMA models
  • Computing forecasts for ARIMA models
  • ARIMA vs ETS models

Pre-seminar activities

Read Sections 9.8-9.10 of the textbook and watch all embedded videos

{r} # #| output: asis # show_slides(week) #

Seminar activities

  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

Seminar code

week9.R

Assignments

  • IA3 is due on Monday 04 May.
  • GA3 is due on Monday 11 May.
  • IA4 is due on Monday 18 May.

Weekly quiz