Week 2: Time series graphics

Tutorial exercises

Complete Exercises 1-4 from Section 2.10 of the book.
We completed Exercise 5 in last week’s lectorial.
Tutorial learning objectives.
Week 2 Tutorial Solution.html

What you will learn this week

Different types of plots for time series including time plots, season plots, subseries plots, lag plots and ACF plots.
The difference between seasonal patterns and cyclic patterns in time series.
What is “white noise” and how to identify it.

Pre-seminar activities

Read Chapter 2 of the textbook and watch all embedded videos

Slides for seminar

Lectorial activities

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?
Which time plot corresponds to which ACF plot?

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))

Seminar code

Assignments

IA1 is due on Monday 10 March.
GA1 is due on Monday 24 March.