Week 6: Exponential smoothing
Week 6
Extend ETS to handle seasonality and automatic model selection using the AICc.
Quiz highlight
Week 6 quiz
Due Wednesday 15 April
Quiz links switch with your selected course, so this stays as the main weekly action.
Prepare before class
Read Sections 8.3 to 8.7 of FPP3 with attention to seasonal ETS structure and automatic selection.
Seminar and workshop
Wednesday 15 April · Seminar 3:00pm to 3:50pm, workshop 4:00pm to 4:50pm · Building K Level 3, K321
Focus
- Seasonal exponential smoothing
- ETS model classes
- Automatic model selection with the AICc
Assessment
Week 6 quiz on Wednesday 15 April
GA2 on Tuesday 14 April · Week 6 quiz on Wednesday 15 April
What you will learn this week
- Exponential smoothing methods with trend and seasonality
- ETS models
- Automatic model selection using the AICc
Pre-class activities
Read Sections 8.3-8.7 of the textbook and watch all embedded videos.
Slides for seminar
Seminar Code
Tutorial exercises
NoteTutorial Learning Objectives
- Compare alternative ETS model structures.
- Select and fit different ETS models (e.g., ETS(A,N,N) vs ETS(A,A,N)).
- Evaluate and compare their fit (e.g., RMSE).
- Interpret forecast differences and prediction intervals, and justify model choice based on empirical performance.
- Select and justify appropriate ETS models for seasonal data.
- Able to identify when multiplicative seasonality (and optional damped trend) is necessary.
- Compare multiple forecasting methods using training/test splits and cross-validation.
- Use training/test sets and cross-validation to fit and compare a range of methods (ETS, transformed ETS, seasonal naïve, STL + ETS).
- Evaluate ETS method and identify its limitations.
- Apply ETS model, assess forecasting quality, and recognise cases where ETS may not perform well and explain why.
- Complete Exercises 5, 7, 11, 14 from Section 8.8 of the book.
Assignments
Weekly quiz
- Week 6 quiz: Open ETF3231 quiz Open ETF5231 quiz — Due Wednesday 15 April.