Week 2: Time series graphics

Week 2

Use graphical tools to surface trend, seasonality, noise, and structural features in time series data.

Quiz highlight

Week 2 quiz

Due Wednesday 11 March

Quiz links switch with your selected course, so this stays as the main weekly action.

Open ETF3231 quizOpen ETF5231 quiz
Prepare before class

Read Chapter 2 of FPP3 and arrive ready to interpret time plots, season plots, lag plots, and ACF displays.

Seminar and workshop

Wednesday 11 March · Seminar 3:00pm to 3:50pm, workshop 4:00pm to 4:50pm · Building K Level 3, K321

Focus
  • Time plots, season plots, subseries plots, lag plots, and ACF plots
  • Reading seasonal structure versus cycles
  • Recognising white noise
Assessment

Week 2 quiz on Wednesday 11 March · IA1 on Friday 13 March

Week 2 quiz on Wednesday 11 March · IA1 on Friday 13 March

Workshop ActivitiesSlides PDFFPP3 Chapter 2Recordings

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-class activities

Slides for seminar

Download pdf

Seminar Code

week2-code.R

Tutorial exercises

NoteTutorial Learning Objectives
  1. Apply time series visualisation techniques.
    • Able to create and interpret a range of graphical displays (time plots, seasonal plots, lag plots, and ACF plots) to explore patterns in time series data.
  2. Identify and explain key time series features.
    • Analyse visual outputs to detect trend, seasonality, autocorrelation, structural changes, and outliers.
  3. Identify white noise behaviour.
    • Use differenced data and ACF plots to determine whether a time series exhibits characteristics of white noise.

Assignments

  • IA1 is due on Friday 13 March.
  • GA1 is due on Tuesday 24 March.

Weekly quiz