Time series graphics

11:00-12:30

Date

17 September 2023

Lab sessions

Lab Session 3

  1. Look at the quarterly tourism data for the Snowy Mountains

    snowy <- tourism |> filter(Region == "Snowy Mountains")
    • Use autoplot(), gg_season() and gg_subseries() to explore the data.
    • What do you learn?

  2. Produce a calendar plot for the pedestrian data from one location and one year.

Lab Session 4

We have introduced the following functions: gg_lag and ACF. Use these functions to explore the four time series: Bricks from aus_production, Lynx from pelt, Close price of Amazon from gafa_stock, Demand from vic_elec. Can you spot any seasonality, cyclicity and trend? What do you learn about the series?

Lab Session 5

You can compute the daily changes in the Google stock price in 2018 using

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))

Does diff look like white noise?