Outline

Overview

This unit will study basic concepts and methods of time series analysis applicable in many real world problems applicable in numerous fields, including economics, finance, insurance, physics, ecology, chemistry, computer science and engineering. This unit will investigate the basic methods of modelling and analyzing of time series data (ie. Data containing serially dependence structure). This can be achieved through learning standard time series procedures on identification of components, autocorrelations, partial autocorrelations and their sampling properties. After setting up these basics, students will learn the theory of stationary univariate time series models including ARMA, ARIMA and SARIMA and their properties. Then the identification, estimation, diagnostic model checking, decision making and forecasting methods based on these models will be developed with applications. The spectral theory of time series, estimation of spectra using periodogram and consistent estimation of spectra using lag-windows will be studied in detail. Further, the methods of analyzing long memory and time series and heteroscedastic time series models including ARCH, GARCH, ACD, SCD and SV models from financial econometrics and the analysis of vector ARIMA models will be developed with applications. By completing this unit, students will develop the essential basis for further studies, such as financial econometrics and financial time series. The skills gain through this unit of study will form a strong foundation to work in a financial industry or in a related research organization.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	STAT2X11 and (MATH1X03 or MATH1907 or MATH1X23 or MATH1933)
Corequisites ?	None
Prohibitions ?	STAT4025
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Shelton Peiris, shelton.peiris@sydney.edu.au
Tutor(s)	Eugene Seneta, eugene.seneta@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam	Final take home exam Written take home exam - max 16 pages	60%	- Due date: 22 Jun 2020 at 11:00	2 hours
Outcomes assessed:
Small test	Comp A 4 weekly reports 4% and a Compute quiz (QC) worth 6%	10%	Multiple weeks	See Canvas
Outcomes assessed:
Small continuous assessment	Comp B 4 weekly reports 4% and a Compute based assignment worth 6%	10%	Multiple weeks	See Canvas
Outcomes assessed:
Online task	Quiz 1 Completed online	10%	Week 07	1 hour
Outcomes assessed:
Assignment	Quiz 2 Assignment - online submission	10%	Week 12	See Canvas
Outcomes assessed:

Assessment summary

Quiz 1: You are allowed to bring one two-sided A4 sheet of handwritten notes as well as a University approved non-programmable calculator. This will be done online, in week7 during your lecture time on Thursday. Worth 10%.
Comp A: This consists of 2 subcomponents during Weeks 2-7: online submission of 4 weekly reports 4% and a Compute quiz (QC) worth 6%. The QC is an online computer exam/quiz (Open book). This must be done online, in week 7, in your assigned computer class time on Monday.
Quiz 2: This will be an assignment worth 10%. Online submission is due in week 12.
Comp B: This consists of 2 subcomponents during Weeks 8-13: online submission of 4 weekly reports 4% and a Compute based assignment worth 6%. Online submission is due in week13.

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
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Time series data, components of a time series; 2. Filtering to remove trends and seasonal components	Lecture and tutorial (3 hr)
Week 02	1. Stationarity time series; 2. Autocorrelation function (ACF) and Partial autocorrelation function (PACF) and their properties; 3. Sample autocorrelations and partial autocorrelations; 4. White noise process and Probability models for stationary time series.	Lecture and tutorial (4 hr)
Week 02	1. Stationarity time series; 2. Autocorrelation function (ACF) and Partial autocorrelation function (PACF) and their properties; 3. Sample autocorrelations and partial autocorrelations; 4. White noise process and Probability models for stationary time series.	Computer laboratory (1 hr)
Week 03	1. Moving Average (MA) models and properties; 2. Invertibility of MA models; 3. Autoregressive (AR) models and their properties; 4. Stationarity of AR models	Lecture and tutorial (4 hr)
Week 03	1. Moving Average (MA) models and properties; 2. Invertibility of MA models; 3. Autoregressive (AR) models and their properties; 4. Stationarity of AR models	Computer laboratory (1 hr)
Week 04	1. Mixed Autoregressive Moving Average (ARMA) models and their properties; 2. Homogeneous nonstationary time series (HNTS). Simple models for HNTS; 3. Autoregressive Integrated Moving Average (ARIMA) models and related results; 4. Review of theoretical patterns of ACF and PACF for AR, MA and ARMA processes; 5. Identification of possible AR, MA, ARMA and ARIMA models for a set of time series data	Lecture and tutorial (4 hr)
Week 04	1. Mixed Autoregressive Moving Average (ARMA) models and their properties; 2. Homogeneous nonstationary time series (HNTS). Simple models for HNTS; 3. Autoregressive Integrated Moving Average (ARIMA) models and related results; 4. Review of theoretical patterns of ACF and PACF for AR, MA and ARMA processes; 5. Identification of possible AR, MA, ARMA and ARIMA models for a set of time series data	Computer laboratory (1 hr)
Week 05	1. Estimation and fitting ARIMA models via MM and MLE methods; 2. Hypothesis testing, diagnostic checking and goodness-of-fit tests. AIC for ARIMA models; 3. Introduction to forecasting methods for ARIMA models	Lecture and tutorial (4 hr)
Week 05	1. Estimation and fitting ARIMA models via MM and MLE methods; 2. Hypothesis testing, diagnostic checking and goodness-of-fit tests. AIC for ARIMA models; 3. Introduction to forecasting methods for ARIMA models	Computer laboratory (1 hr)
Week 06	1. Minimum mean square error (mmse) forecasting and its properties; 2. Derivation of l-step ahead mmse forecast function. Forecast updates; 3. Forecast errors, related results and applications	Lecture and tutorial (4 hr)
Week 06	1. Minimum mean square error (mmse) forecasting and its properties; 2. Derivation of l-step ahead mmse forecast function. Forecast updates; 3. Forecast errors, related results and applications	Computer laboratory (1 hr)
Week 07	1. An introduction to spectral theory of time series; 2. Spectral density function (sdf) of an ARMA model; 3. Examples	Lecture and tutorial (4 hr)
Week 07	1. An introduction to spectral theory of time series; 2. Spectral density function (sdf) of an ARMA model; 3. Examples	Computer laboratory (1 hr)
Week 08	1. Estimation of the sdf using the periodogram; 2. Sampling properties of the periodogram; 3. Smoothed periodogram estimators for the sdf	Lecture and tutorial (4 hr)
Week 08	1. Estimation of the sdf using the periodogram; 2. Sampling properties of the periodogram; 3. Smoothed periodogram estimators for the sdf	Computer laboratory (1 hr)
Week 09	1. An introduction to fractional differencing and long memory time series modelling; 2. Estimation of ARFIMA(p,d,q); 3. Applications of ARFIMA	Lecture and tutorial (4 hr)
Week 09	1. An introduction to fractional differencing and long memory time series modelling; 2. Estimation of ARFIMA(p,d,q); 3. Applications of ARFIMA	Computer laboratory (1 hr)
Week 10	1. Generalised fractional processes. Gegenbaur processes; 2. Spectral properties of Gegenbauer processes; 3. Estimation of parameters of Gegenbauer models	Lecture and tutorial (4 hr)
Week 10	1. Generalised fractional processes. Gegenbaur processes; 2. Spectral properties of Gegenbauer processes; 3. Estimation of parameters of Gegenbauer models	Computer laboratory (1 hr)
Week 11	1. Topics from financial time series/econometrics: Conditional heteroscedasticity; 2. ARCH, GARCH processes for heavy tailed data and their properties; 3. Stochastic volatility models and their properties	Lecture and tutorial (4 hr)
Week 11	1. Topics from financial time series/econometrics: Conditional heteroscedasticity; 2. ARCH, GARCH processes for heavy tailed data and their properties; 3. Stochastic volatility models and their properties	Computer laboratory (1 hr)
Week 12	An introduction to VAR and vector ARIMA models. Spectral properties. Estimation.	Lecture and tutorial (4 hr)
Week 12	An introduction to VAR and vector ARIMA models. Spectral properties. Estimation.	Computer laboratory (1 hr)
Week 13	State-space models and their properties. Quasi Maximum Likelihood Estimation (QMLE)	Lecture and tutorial (4 hr)
Week 13	State-space models and their properties. Quasi Maximum Likelihood Estimation (QMLE)	Computer laboratory (1 hr)

Attendance and class requirements

Due to the exceptional circumstances caused by the COVID-19 pandemic, attendance requirements for this unit of study have been amended. Where online tutorials/workshops/virtual laboratories have been scheduled, students should make every effort to attend and participate at the scheduled time. Penalties will not be applied if technical issues, etc. prevent attendance at a specific online class. In that case, students should discuss the problem with the coordinator, and attend another session, if available.

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

Analysis of Financial Time Series, R.S.Tsay, John Wiley 3rd Edition (2010).
The Analysis of Time Series: An Introduction with R, Chris Chatfield, Haipeng Xing, Chapman and Hall/CRC, 7th Edition (2019).

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. 1.Explain and examine time series data and identify components of a time series; remove trends, seasonal and other components.
LO2. Identify stationarity time series; sample autocorrelations and partial autocorrelations, probability models for stationary time series.
LO3. Explain homogeneous nonstationary time series, simple and integrated models and related results.
LO4. Apply estimation and fitting methods for ARIMA models via MM and MLE methods.
LO5. Apply hypothesis testing, diagnostic checking and goodness-of-fit tests methodology.
LO6. Construct forecasting methods for ARIMA models.
LO7. Explain spectral methods in time series analysis.
LO8. Apply financial time series and related models to straightforward problems.
LO9. Apply the methods of analysis of GARCH and other models for volatility.
LO10. Explain and apply methods of vector time series models

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.

Added new reference books.

Additional information

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

No eating or drinking is allowed in any laboratory under any circumstances
A laboratory coat and closed-toe shoes are mandatory
Follow safety instructions in your manual and posted in laboratories
In case of fire, follow instructions posted outside the laboratory door
First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory
As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

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

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect

Current students

STAT3925: Time Series (Advanced)

Semester 1, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Work, health and safety

Disclaimer

On this page

Useful links

Media

Student links

About us

Connect