1. We have introduced various functions for time series graphics including autoplot(), gg_season(), gg_subseries(), gg_lag() and ACF. Use these functions to explore the quarterly tourism data for the Snowy Mountains.

    snowy <- tourism |> filter(Region == "Snowy Mountains")

    What do you learn?

  2. Which time plot corresponds to which ACF plot?

  1. You can compute the daily changes in the Google stock price in 2018 using the code below. Do the daily changes look like white noise?
dgoog <- gafa_stock |>
  filter(Symbol == "GOOG", year(Date) >= 2018) |>
  mutate(trading_day = row_number()) |>
  update_tsibble(index=trading_day, regular=TRUE) |>
  mutate(diff = difference(Close))