Outline

Overview

Predict the future by mastering the art of time series analysis. Understanding and modelling time series data is important in a wide range of domains, for example, energy demand, retail sales, healthcare, web traffic, weather, finance, and economics. This unit will equip you with the knowledge and tools to confidently tackle the complexities of time series data and apply modern forecasting techniques using real-world data. Each week brings a new set of challenges, guiding you through methods like exponential smoothing, ARIMA, and dynamic regression models. You will learn how to effectively visualise and communicate data collected over time, fit a variety of models and assess their performance, choose between completing models, and quantify the uncertainty around your forecasts. Through practical assignments and projects, you will hone your ability to learn from the past and make reliable, evidence-based predictions about the future.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Fundamentals of statistics and coding in R, e.g. ODAT5011: Data Analysis Foundations. It would be an advantage to also take ODAT5021 to further build your statistical and computational skills before attempting ODAT5022.
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Garth Tarr, garth.tarr@sydney.edu.au
Lecturer(s)	Rajan Shankar, rajan.shankar@sydney.edu.au

Assessment

The census date for this unit availability is 9 May 2025

Details
Criteria

Type	Description	Weight	Due	Length
Assignment	Assignment 1 Directed questions on given data	20%	Week 03 Due date: 11 May 2025 at 23:59 Closing date: 25 May 2025	5 pages
Outcomes assessed:
Assignment	Assignment 2 Directed questions on given data	20%	Week 05 Due date: 25 May 2025 at 23:59 Closing date: 08 Jun 2025	5 pages
Outcomes assessed:
Assignment	Project: presentation Oral presentation	20%	Week 07 Due date: 05 Jun 2025 at 18:00 Closing date: 19 Jun 2025	20 slides, 20 seconds per slide
Outcomes assessed:
Assignment	Project: report Written report	20%	Week 08 Due date: 15 Jun 2025 at 23:59 Closing date: 29 Jun 2025	5 pages
Outcomes assessed:
Participation	Workshop participation Participation in workshops and submission of work	10%	Weekly	2 pages
Outcomes assessed:
Small test	Online quizzes Weekly online quizzes	10%	Weekly	1 hour
Outcomes assessed:
= AI allowed ?

Assessment summary

Workshop participation: you will attend and engage in the workshop and then submit your attempt at the workshop questions in Canvas afterwards. If you can't join a workshop in a given week you have until Sunday 11:59pm following the workshop to submit your attempt at the workshop questions through Canvas.

Online quizzes: each week there will be a Canvas quiz due by Sunday 11:59pm. You can attempt the quiz as many times as you like but marks won't be released until after the due date.

Assignments: the two assignments are designed to test your understanding of specific topics covered in the unit and provide feedback on your progress. The questions are more specific and directed than the project.

Project: the project is more open-ended data exploration than the two assignments. You will design, implement and compare forecasting models using real-world data of your choosing. There are two components to the project. For the presentation component you will present to staff and other students and get feedback on your approach. For the report component you will write up a final report using the same data set as the presentation. You might make changes to your approach between the presentation and the report based on feedback.

Assessment criteria

Result name	Mark range	Description
High distinction	85 - 100	Work of outstanding quality, demonstrating mastery of the learning outcomes assessed. The work shows significant innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or exceptional skill.
Distinction	75 - 84	Work of excellent quality, demonstrating a sound grasp of the learning outcomes assessed. The work shows innovation, experimentation, critical analysis, synthesis, insight, creativity, and/or superior skill.
Credit	65 - 74	Work of good quality, demonstrating more than satisfactory achievement of the learning outcomes assessed, or work of excellent quality for a majority of the learning outcomes assessed.
Pass	50 - 64	Work demonstrating satisfactory achievement of the learning outcomes assessed.
Fail	0 - 49	Work that does not demonstrate satisfactory achievement of one or more of the learning outcomes assessed.

For more information see guide to grades.

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

Except for supervised exams or in-semester tests, you may use generative AI and automated writing tools in assessments unless expressly prohibited by your unit coordinator.

For exams and in-semester tests, the use of AI and automated writing tools is not allowed unless expressly permitted in the assessment instructions.

The icons in the assessment table above indicate whether AI is allowed – whether full AI, or only some AI (the latter is referred to as “AI restricted”). If no icon is shown, AI use is not permitted at all for the task. Refer to Canvas for full instructions on assessment tasks for this unit.

Your final submission must be your own, original work. You must acknowledge any use of automated writing tools or generative AI, and any material generated that you include in your final submission must be properly referenced. You may be required to submit generative AI inputs and outputs that you used during your assessment process, or drafts of your original work. Inappropriate use of generative AI is considered a breach of the Academic Integrity Policy and penalties may apply.

The Current Students website provides information on artificial intelligence in assessments. For help on how to correctly acknowledge the use of AI, please refer to the  AI in Education Canvas site.

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.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Introduction to time series	Independent study (4 hr)
Week 01	Introduction to time series	Workshop (1.5 hr)
Week 02	Regression methods for forecasting	Independent study (4 hr)
Week 02	Regression methods for forecasting	Workshop (1.5 hr)
Week 03	Exponential smoothing	Independent study (4 hr)
Week 03	Exponential smoothing	Workshop (1.5 hr)
Week 04	ARIMA	Independent study (4 hr)
Week 04	ARIMA	Workshop (1.5 hr)
Week 05	ARIMA	Independent study (4 hr)
Week 05	ARIMA	Workshop (1.5 hr)
Week 06	Dynamic regression models	Independent study (4 hr)
Week 06	Dynamic regression models	Workshop (1.5 hr)

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, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia. OTexts.com/fpp3.

Learning outcomes

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.

Outcomes
Graduate qualities

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

LO1. explain and compare different forecasting methods in terms of their assumptions and applicability across various types of time series data
LO2. discuss the challenges of implementing forecasting techniques in practice
LO3. apply appropriate statistical techniques to conduct time series forecasting, utilising software tools to visualise, model, predict, and interpret given data
LO4. evaluate and communicate the accuracy and effectiveness of forecast models
LO5. design and implement a time series forecasting project using real-world data

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
Learning outcomes

Responding to student feedback

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

This is the first time this unit has been offered.

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ODAT5022: Applied Time Series Analysis

PG Online Session 1B, 2025 [Online] - Online Program

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links