ETC3550 / ETC5550 Applied Forecasting (Sem 1 2021)


Difficulty:

Year Completed: Semester 1, 2021

Prerequisite: ETC2410 or ETC2420

(or ETC3440, or ETF2100, or ETW2510)

 

Exemption:

CS2 Risk Modelling and Survival Analysis

ETC2420 (10%), ETC3420 (20%), ETC3430 (50%), ETC3550 (20%)

Weighted average of 70% required. Minimum of 60% required for each unit.


Mean Setu Score: 93.2%

 

Clarity of Learning Outcomes: 94.2%

Clarity of Assessments: 93.4%

Feedback: 89.8%

Resources: 94.2%

Engagement: 92%

Satisfaction: 94.2%


Subject Content:

Lecture(s) and Tutorial(s):

Textbook(s):

Assessments:

 

This unit focuses on several different models. The models covered

in this unit include: STL decompositions, Exponential Smoothing,

ARIMA models and Dynamic regressions. During this unit you will

learn which model is most appropriate for a given data set and

how the model can be specified using R. The unit also focuses on

comparing models and analysing which model is best for

forecasting using AIC.

 

1 x 2 hour lecture

1 x 1 hour tutorial

 

The textbook is given for free to students. The textbook can be

useful for some further reading and all of the tutorial questions

are taken from the textbook.

 

Weekly Assignment 8 x 2 or 4%

Retail Forecast Project 20%

Final Exam (3 hours) 60%


Comments

The unit was one of the most enjoyable units I have undertaken in

my undergraduate degree. The content throughout the unit is

very applicable and relevant to the real world. Some of the data

used in assignments comes directly from work that the lecturer

has consulted on. At the beginning of the unit the lecturer gave

us problems he had encountered during his consulting work. By

the end of the unit I was able to use the concepts learnt to help

solve those problems.

 

The lectures were engaging and had a good mix of coding and

content. The lecturer made an effort to explain the lines of the

code to ensure students were understanding what he was doing

throughout the lecture. The lecture slides includes all relevant

materials for the unit however the textbook can be used for

further clarification.

  

The tutorials would go through the assigned questions from each

chapter of the textbook. Specific focus was placed on the

questions that related closely to the weekly assignment. The

tutorials were well structured. They focused on covering

questions that were very relevant to the topic as well as relevant

for completing the weekly assignments. In order to achieve full

marks in weekly assignments tutorial attendance was required as

tutorials would often break down the code to help with

understanding. Similar to the concepts learnt the tutorial

questions were very applicable to the real-world.

Weekly assessments were a great way to assess a students ability

to utilise R to help them achieve their desired outcome.

Assignments also help build the necessary skills to tackle the

larger individual project at the end of the semester. It is also

important to practice the skills in the assignments as the exam

had a coding component to it.

One of the most applicable and interesting units that I have taken.

The enjoyment I experienced in this unit can be attributed to the

amazing teaching team who were continually helpful throughout

the semester and great at helping other students through the

Moodle forums.

General Overview:

Lectures:

Tutorials:

Assessments/Other Assessments

Concluding Remarks