Week 5: Exponential smoothing

What you will learn this week

  • Simple exponential smoothing and corresponding ETS models
  • Methods with trend and corresponding ETS models
  • ETS model notation

Pre-seminar activities

Read Sections 8.1-8.4 of the textbook and watch all embedded videos

Tutorial exercises

NoteTutorial Learning Objectives
  1. Implement and interpret exponential smoothing models.
    • Able to estimate simple exponential smoothing (SES) model, interpret the smoothing parameters, and generate point and interval forecasts.
  2. Develop custom forecasting functions.
    • Write and test user-defined functions to perform simple exponential smoothing, compare their outputs with built-in functions (like ETS()), and understand the computational steps involved.
  3. Optimise model parameters using numerical methods.
    • Modify exponential smoothing functions to compute an error measure and use numerical optimisation methods to determine the optimal smoothing parameter
    • Able to compare results with the automatic ETS estimation.

Assignments

  • IA2 is due on Thursday 02 April.
  • GA2 is due on Tuesday 14 April.
  • IA3 is due on Friday 24 April.

Weekly quiz