ETC3550 / ETC5550 Applied Forecasting


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Difficulty:

Year Completed: Semester 1, 2020

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: 94.6%

 

Clarity of Learning Outcomes: 94.8%

Clarity of Assessments: 92.6%

Feedback: 91%

Resources: 95%

Engagement: 96.4%

Satisfaction: 95.8%


Subject Content:

Lecture(s) and Tutorial(s):

Textbook(s):

Assessments:

 

This unit covered an introduction to forecasting and R, seasonal

adjustments and decompositions, exponential smoothing,

ARIMA models, multiple regression and forecasting, dynamic

regression and advanced methods.

 

1 x 2 hour lecture

1 x 1 hour tutorial

 

Forecasting: Principles and Practice - Free and very useful. Not

compulsory for reading but tutorial questions come from the

textbook. The textbook contains well written content, examples,

and R codes to follow, and is very useful for revision after lectures.

 

Weekly Assignment 2 x 10%

Retail Forecast Project 20%

Final Exam (3 hours) 60%


Comments

This unit is one of the best units offered in Monash. The lecturer

Rob Hyndman and tutors were very helpful and responsive. The

content and R code is extremely practical and easy to follow.

 

The lectures must be watched as they contain examples and

solutions to questions and the R code that needs to be used.

Content is delivered very well with mostly real-life examples that

are clear. Lectures were very engaging. It is also a good place for

questions as Rob can demonstrate the solutions live.

 

Tutorials are not compulsory but are good to attend. No

preparation is needed except for having watched the previous

lecture. Tutorials do not cover new content but do cover more

questions and solutions to them and therefore new code that

might be useful. They focus on practical examples of R

programming and problem solving. Tutorial contents are based

on the lecture and questions are taken from the book. Most of the

tutorial is independent group work on the questions and tutors

giving advice on how to approach them. They are extremely

helpful in learning R syntax and consolidating the lecture

material. However, the tutorials can feel a little short and often

not all questions can be attempted. The solutions and code for

each question are posted after tutorials.

 

Weekly Assignments: - 10 short assignments that generally

require short R code or analysis. Leniently marked and easy to

achieve full marks.

Retail Forecast Project: - Individual forecasting report based on

random retail data to be analysed using R. Straightforward but as

it is random the answers might not be what you expect. A good

understanding of course material and R is required. There was

generous help given by tutors especially on R programming. It was

a very practical assignment.

The exam was moderately difficult and fair. It was not very

mathematics heavy, but more analytical with only theory and no R

component. Practice exams were provided and reflected the

actual exam quite well.

 

A unit worthy to be your elective if you have the space.

General Overview:

Lectures:

Tutorials:

Assessments/Other Assessments

Concluding Remarks