ETF3231/5231 Business Forecasting

Lecturer/Chief Examiner

George Athanasopoulos.

Email: George.Athanasopoulos@monash.edu

Consultations

Weekly schedule

Week Topic Chapter Assessments
03 Mar Introduction to forecasting and R 1. Getting started
10 Mar Time series graphics 2. Time series graphics IA1
17 Mar Time series decomposition 3. Time series decomposition
24 Mar The forecaster’s toolbox 5. The forecaster’s toolbox GA1
31 Mar Exponential smoothing 8. Exponential smoothing
07 Apr Exponential smoothing 8. Exponential smoothing IA2
14 Apr ARIMA models 9. ARIMA models GA2
21 Apr Mid-semester break
28 Apr ARIMA models 9. ARIMA models IA3
05 May ARIMA models 9. ARIMA models GA3
12 May Multiple regression and forecasting 7. Time series regression models
19 May Dynamic regression 10. Dynamic regression models IA4
26 May Revision Revision GA4

Assessments

Note

Final exam 60%, 4x (ETF3231)/8x (ETF5231) assignments 40%

IA: denotes an individual assignment to be completed by all students.

GA: denotes a group assignment to be completed only by ETF5231 students

The due dates will be confirmed throughout the semester as our speed in covering topics will vary depending on student needs.
Please note: In these assessments, you must not use generative artificial intelligence (AI) to generate any materials or content in relation to the assessment tasks.

Weeks Assessment Task Weight
2 IA1 5%
4 GA1 5%
6 IA2 7%
7 GA2 7%
8 IA3 10%
9 GA3 10%
11 IA4 18%
12 GA4 18%

ETF5231 students: marks allocated to Assignments will be a combination between Individual Assignments and Group Assignments with a weights 0.7 and 0.3. So if you score 8/10 for IA3 and 5/10 for GA3 your mark for Assignment 3 will be 8*0.7+5*0.3=7.1 out of 10. Hence, it is important to make sure that groups are performing at the highest standard.

Presentation requirements: using Rmarkdown, and should converted the assignment into pdf output file unless otherwise specified.

Criteria for marking: Provided the task has been completed and your code runs without errors, full marks will be awarded. Marks will be deducted for incomplete tasks or if there are errors.

Penalties for late lodgement: The University has a standard penalty for late submission. See the Marking and Feedback Procedure for more information

Additional information: Additional information regarding this assessment will be provided during the scheduled class time and on Moodle.

Group Participation - ETF5231 Students ONLY

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.

R package installation

Here is the code to install the R packages we will be using in this unit.

install.packages(c("tidyverse","fpp3", "GGally"), dependencies = TRUE)

Textbook

Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: principles and practice. OTexts.