activities – ETF3231/ETF5231 Business Forecasting

We have introduced various functions for time series graphics including autoplot(), gg_season(), gg_subseries() and ACF(). Use these functions to explore the quarterly tourism data for the Snowy Mountains.
```
snowy <- tourism |>
   filter(Region == "Snowy Mountains") |>
   summarise(Trips = sum(Trips))
```
What do you learn?
Which time plot corresponds to which ACF plot?

You can compute and plot the daily changes in the Amazon stock price in 2018 using the code below. Do the daily changes look like white noise?

gafa_stock |>
  filter(Symbol == "AMZN", year(Date) >= 2018) |>
  mutate(trading_day = row_number()) |>
  update_tsibble(index=trading_day, regular=TRUE) |>
  mutate(diff = difference(Close)) |> 
  autoplot()