ETF3231/5231 Business Forecasting
Business Forecasting focuses on practical forecasting workflows with R, from first visual diagnostics through ETS, ARIMA, and regression-based models. The site is organised the same way as the week-to-week teaching: use the current-week panel to get oriented, then drop into the relevant week page for readings, seminar slides, activities, and assessment reminders.
This week
Week 6 · Exponential smoothing
Extend ETS to handle seasonality and automatic model selection using the AICc.
Week 6
Due Wednesday 15 April
Open the full week page for the reading sequence, workshop flow, and quiz context.
Read Sections 8.3 to 8.7 of FPP3 with attention to seasonal ETS structure and automatic selection.
Wednesday 15 April · Seminar 3:00pm to 3:50pm, workshop 4:00pm to 4:50pm · Building K Level 3, K321
- Seasonal exponential smoothing
- ETS model classes
- Automatic model selection with the AICc
Week 6 quiz on Wednesday 15 April
GA2 on Tuesday 14 April · Week 6 quiz on Wednesday 15 April
Coming up next: Week 7 starts 22 Apr: ARIMA models
Start here Moodle Announcements, assessment instructions, unit administration, and official submission links. Watch Recordings Lecture recordings and supporting video material for each teaching week. Ask questions Discussion Use Ed for clarifications, course questions, and help between classes. Plan ahead Weekly schedule See the sequence of topics, textbook chapters, quizzes, and assignment deadlines.
Unit at a glance
Seminar and workshop Wednesday 3:00pm to 4:50pm Building K Level 3, K321, Caulfield campus.
Tutorials Thursday to Friday Weekly 50-minute tutorial classes across multiple streams.
Core method set ETS, ARIMA, regression Move from graphical diagnostics through benchmark, state space, and regression-based forecasting.
Assessment Quizzes, IAs, final exam Quizzes, IAs, GAs, final exam In-semester work is quizzes plus individual assignments; the final exam contributes 60%. In-semester work includes quizzes, individual assignments, and group assignments; the final exam contributes 60%.
Learning outcomes
Diagnose the main features of time series data using graphics, decomposition, and residual analysis.
Build and compare forecasting models ranging from benchmark methods to ETS and ARIMA families.
Use predictors, regression structure, and dynamic regression to improve forecasts when extra information is available.
Communicate forecasts clearly in Quarto-based analytical reports and reproducible R workflows.
Teaching team and support
Who is teaching this unit
- George Athanasopoulos. Lecturer and Chief Examiner. Email: George.Athanasopoulos@monash.edu
- Ari Handayani, tutor
- Joan Tan, tutor
- Sombut Jaidee, tutor
- Weiben Zhang, tutor
Weekly support options
- Monday 12 to 1pm. Joan Tan. Room H4.68 and Zoom.
- Tuesday 11 to 12:30pm. Ari Handayani. Room H4.63 and Zoom.
- Tuesday 4 to 5pm. Weiben Zhang. Zoom.
- Thursday 11 to 11:30am. Ari Handayani. Zoom.
- Thursday 2 to 3pm. George Athanasopoulos. Zoom.
- Friday 1 to 2pm. Ari Handayani. Zoom.
- Friday 5 to 6pm. Sombut Jaidee. Room H4.69 and Zoom.
Textbook and software setup
- Forecasting: Principles and Practice is the main text for the unit.
-
Install the core packages with
install.packages(c(“tidyverse”,“fpp3”,“GGally”), dependencies = TRUE). - Use the weekly pages for the exact reading sections and preparation tasks.
Weekly schedule
Each week combines textbook reading, video preparation, the Wednesday seminar and workshop block, and tutorial support. Use the week pages for readings, slides, activities, and upcoming deadlines.
Assessments
Course structure ETF3231 has no group assignments Your in-semester assessment is quizzes plus the four individual assignments.
Weekly rhythm Workshop quizzes Weekly quizzes completed during workshops, with the best eight contributing the core 8%. Final hurdle Final exam The exam contributes 60% of the total mark, with in-semester work contributing the remaining 40%.
ETF3231 students complete the quiz stream plus the individual assignments only. The due dates can move slightly as the semester unfolds, so Moodle remains the final source of truth for submission timing. ETF5231 students combine individual and group assignment results with weights 0.7 and 0.3 respectively. The due dates can move slightly as the semester unfolds, so Moodle remains the final source of truth for submission timing.
Presentation requirements remain Quarto-based unless otherwise specified, and AI use must be declared according to the assessment instructions. Standard Monash late penalties apply; see the Marking and Feedback Procedure for the formal policy.
Group participation rules for group assignments (GAs): It is expected that all group members participate equally to the group assignments. It sometimes helps to keep a log of the tasks each member is to complete. This may assist in keeping some balance throughout the semester. To ensure that all group members are contributing the following rules will apply for all the group assignments throughout the semester.
Every group member has the right to express concern/dislike in terms of a group member being absent from group activities. This has to be expressed directly to me (and only me) via a formal email stating facts that have hindered the operations of the group (saying I do not like George because he is tall is not a reason for expressing concern). If at least two such complaints are submitted during the completion of an assignment, the group member concerned will receive a warning. If the situation is not improved during the next assignment and another two complaints are again submitted, the group member will be immediately removed from the group and will be expected to complete the rest of the group assignments alone.
Install the R packages
Use the following once if you need the core packages for the semester.
install.packages(c("tidyverse","fpp3", "GGally"), dependencies = TRUE)