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Unit outline_

QBUS3850: Time Series and Forecasting

Semester 1, 2023 [Normal day] - Remote

Time series and dynamic modelling is a fundamental component of modern business practice. Further, forecasting is a required component of business decision making. This unit provides an introduction to the time series models used for the analysis of data arising in different business areas including finance, accounting, marketing, economics and many other disciplines. It then considers methods for point and interval forecasting, testing and sensitivity analyses, in the context of these models. Topics include: the properties of time-series data; Seasonal Exponential smoothing and ARIMA models; Vector Autoregressions; modelling and forecasting conditional volatility, via ARCH and GARCH; forecasting risk measures such as Value at Risk and Expected Shortfall; dynamic factor models. Emphasis is placed on applications involving the analysis of many real business datasets. Students are encouraged to undertake hands-on analysis using appropriate software.

Unit details and rules

Academic unit Business Analytics
Credit points 6
Prerequisites
? 
QBUS2820
Corequisites
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Andrey Vasnev, andrey.vasnev@sydney.edu.au
Lecturer(s) Andrey Vasnev, andrey.vasnev@sydney.edu.au
Type Description Weight Due Length
Assignment Final assignment
Written assignment
40% Formal exam period
Due date: 03 Jun 2023 at 14:00
7 pages including graphs and tables
Outcomes assessed: LO5 LO1 LO2 LO3 LO4
Supervised test
? 
Mid-semester exam
Written exam
20% Week 08
Due date: 17 Apr 2023 at 10:00
1 hour
Outcomes assessed: LO1 LO2 LO3 LO4
Assignment group assignment Group Assignment
n/a
40% Week 13
Due date: 26 May 2023 at 23:59
n/a
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
group assignment = group assignment ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Distinction

75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Credit

65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.

Pass

50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school. 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction Lecture and tutorial (4 hr) LO1
Week 02 Decomposition methods Lecture and tutorial (4 hr) LO1
Week 03 Autoregressive integrated moving average (ARIMA) Lecture and tutorial (4 hr) LO1 LO3
Week 04 Autoregressive integrated moving average (ARIMA) Lecture and tutorial (4 hr) LO1 LO2 LO3 LO4
Week 05 Forecasting with time series regression and Vector Auto Regression (VAR) Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 06 Forecast combinations Lecture and tutorial (4 hr) LO3 LO4
Week 07 1. Autoregressive conditional heteroscedasticity (ARCH) volatility modeling; 2. Generalised Autoregressive conditional heteroscedasticity (GARCH) volatility modeling Lecture and tutorial (4 hr) LO1 LO3 LO4
Week 08 Lecture: Mid-Semester Test; Tutorial: Generalised Autoregressive conditional heteroscedasticity (GARCH) volatility modeling Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 09 Volatility forecasting and volatility proxies Lecture and tutorial (2 hr) LO2 LO3 LO4
Week 10 Financial risk and its measurement Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 11 Forecasting Value at Risk (VaR) Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 12 Forecasting tail risk, VaR and expected shortfall (ES) Lecture and tutorial (4 hr) LO2 LO3 LO4
Week 13 Revision Lecture and tutorial (4 hr) LO2 LO3 LO4

Attendance and class requirements

Lecture recordings: All lectures are recorded and will be available on Canvas for student use. Please note the Business School does not own the system and cannot guarantee that the system will operate or that every class will be recorded. Students should ensure they attend and participate in all classes.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

  • Hyndman, Rob J and George Athanasopoulos, Forecasting: Principles and Practice. (Free online textbook available at http://otexts.com/fpp/)
  • Tsay, R, (2010). Analysis of Financial Time Series, Wiley: New York. 3rd edition.

All further readings for this unit can be accessed through the Library eReserve, available on Canvas.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

  • LO1. understand the characteristics of time-series data in order to analyse real business data of this form
  • LO2. select and use an appropriate technique to predict the future behaviour of business variables of interest
  • LO3. design and estimate of a range of quantitative, statistical models for volatility and risk
  • LO4. appraise the suitability of both models and methods for forecasting business and financial data
  • LO5. develop complex programs in Python software for estimation of time series models and forecasting

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
LO1         
LO2         
LO3         
LO4         
LO5         

This section outlines changes made to this unit following staff and student reviews.

This is the third time this unit has been offered. Student feedback from the previous offering has been noted

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.