Week 4: The forecaster’s toolbox
What you will learn this week
- Forecasting workflow
- Four benchmark forecasting methods that we will use for comparison
- Linear trends, dummy seasonality
- Fitted values, residuals
- Forecasting using transformations
- Forecasting with decompositions
- Forecast evaluation
Pre-seminar activities
- Read Chapter 5 of the textbook and watch all embedded videos
- Read Chapter 7.4 of the textbook and watch the embedded video. Ignore Fourier series for the moment.
Tutorial exercises (in class or on your own)
NoteTutorial Learning Objectives
- Apply and compare simple benchmark forecasting methods.
- Able to select and implement appropriate baseline forecasting methods (e.g., NAIVE, SNAIVE, random walk with drift) for a variety of real-world time series and produce forecast plots.
- Evaluate forecasting performance using simple benchmark forecasting methods.
- Produce and visualise forecasts and evaluate whether a simple benchmark provides a reasonable baseline for forecasting.
- Decompose and forecast with STL methods.
- Apply STL decomposition to extract time series components.
- Able to generate seasonally adjusted forecasts, reseasonalise results, and compare performance with simple benchmark methods while interpreting residuals.
- Complete Exercises 1, 3, 5, 11 from Section 5.11 of the book.
- Complete Exercise 2 from Section 7.10 of the book.