STATS 7008 - Statistics Topic D

North Terrace Campus - Semester 2 - 2024

This course is available for students taking a Masters degree in Mathematical Sciences. The course will cover an advanced topic in statistics. For details of the topic offered this year please refer to the Course Outline.

  • General Course Information
    Course Details
    Course Code STATS 7008
    Course Statistics Topic D
    Coordinating Unit Mathematical Sciences
    Term Semester 2
    Level Postgraduate Coursework
    Location/s North Terrace Campus
    Units 3
    Available for Study Abroad and Exchange Y
    Assessment Ongoing assessment, exam
    Course Staff

    Course Coordinator: David Shorten

    Course Timetable

    The full timetable of all activities for this course can be accessed from Course Planner.

  • Learning Outcomes
    Course Learning Outcomes
    I 2024 the topic of this course is Time Series


    In an era where continually collected data is becoming the norm, an understanding of how to manage temporal autocorrelation is an essential skill for the modern statistician.

    In this course you will learn the basics of time series analysis and extensions to SARIMA, VARMA and ARCH models. You will be introduced to the underpinning mathematical frameworks, learn how to implement the methods in R and produce written summaries of your analysis in the style of professional reports.


    The third year course Statistical Modelling III, or equivalent. Students should also be familiar with R, RStudio and RMarkdown or Quarto.

    Learning Outcomes

    1. Understand the mathematical structures underpinning time series analysis.
    2. Demonstrate the ability to efectively analyse non-stationary, complex, time series data.
    3. Produce professional statistical reports which summarise time series analysis for both specialist and non-specialist audiences.

    University Graduate Attributes

    This course will provide students with an opportunity to develop the Graduate Attribute(s) specified below:

    University Graduate Attribute Course Learning Outcome(s)

    Attribute 1: Deep discipline knowledge and intellectual breadth

    Graduates have comprehensive knowledge and understanding of their subject area, the ability to engage with different traditions of thought, and the ability to apply their knowledge in practice including in multi-disciplinary or multi-professional contexts.


    Attribute 2: Creative and critical thinking, and problem solving

    Graduates are effective problems-solvers, able to apply critical, creative and evidence-based thinking to conceive innovative responses to future challenges.


    Attribute 3: Teamwork and communication skills

    Graduates convey ideas and information effectively to a range of audiences for a variety of purposes and contribute in a positive and collaborative manner to achieving common goals.


    Attribute 4: Professionalism and leadership readiness

    Graduates engage in professional behaviour and have the potential to be entrepreneurial and take leadership roles in their chosen occupations or careers and communities.


    Attribute 5: Intercultural and ethical competency

    Graduates are responsible and effective global citizens whose personal values and practices are consistent with their roles as responsible members of society.


    Attribute 8: Self-awareness and emotional intelligence

    Graduates are self-aware and reflective; they are flexible and resilient and have the capacity to accept and give constructive feedback; they act with integrity and take responsibility for their actions.

  • Learning Resources
    Required Resources
    Reading each week will come from This will be supported with additional resources provided on MyUni.
    Recommended Resources
    There are many good text books on time series, most of which will be relevant and helpful for this course. Some of the ones I have found most usefu include:
    Cowpertwait, Paul S.P., & Metcalfe, Andrew V. (2009). Introductory Time Series with R. Springer. Use R!
    Cryer, Jonathan D., & Chan, Kung-Sik (2008). Time Series Analysis. With Applications in R. Springer Texts in Statistics
    Chatfield, Chris. (2019). The Analysis of Time Series: An Introduction with R. Routledge

    And for those of you who prefer the tidyverse:
    Hyndman, R.J., & Athanasopoulos, G. (2021) Forecasting: principles and practice, 3rd edition, OTexts: Melbourne, Australia.
    Online Learning
    Electronic resources, including lecture notes and assignments, will be posted on MyUni. You will also be encouraged to use discussion boards.
  • Learning & Teaching Activities
    Learning & Teaching Modes
    Each week will require reading of online notes prior to attending a class session. These sessions will explore a mixture of mathematical theory and implementation and notes for these sessions will be provided on MyUni.

    The class size is typically small and you will be encouraged to ask questions and contribute to the discussion.

    You will be asked to peer review work from your classmates if the class size is large enough.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    The information below is provided as a guide to assist students in engaging appropriately with the course requirements.

    Activity Quantity Hours
    Reading 12 36
    Workshops 12 36
    Lierature review 1 10
    Anlaytical methods 1 10
    Report 1 34
    Revision 2 30
    Total 156

    Learning Activities Summary
    Topics Include:

    Time Series Basics
    MA Models, Partial Autocorrelation, Notational Conventions
    Identifying and Estimating ARIMA models; Using ARIMA models to forecast future values
    Seasonal Models
    Smoothing and Decomposition Methods and More Practice with ARIMA models
    The Periodogram
    Regression with ARIMA errors, Cross correlation functions, and Relationships between 2 Time Series
    Prewhitening; Intervention Analysis
    Longitudinal Analysis/ Repeated Measures
    Vector Autoregressive Models/ ARCH Models
    Spectral Analysis
    Fractional Differencing and Threshold Models

  • Assessment

    The University's policy on Assessment for Coursework Programs is based on the following four principles:

    1. Assessment must encourage and reinforce learning.
    2. Assessment must enable robust and fair judgements about student performance.
    3. Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
    4. Assessment must maintain academic standards.

    Assessment Summary
    Assessment Percent of final mark
    Written assignments (3) 45
    Final exam 55

    The due dates for the assignments are as follows: 

    Assignment 1: due August 20 
    Assignment 2: due September 17 
    Assignment 3: due October 29 

    Assessment Related Requirements
    A final aggregate score of at least 50% is required to pass the course.
    Assessment Detail

    No information currently available.

    All assessment is submitted through MyUni.
    Course Grading

    Grades for your performance in this course will be awarded in accordance with the following scheme:

    M10 (Coursework Mark Scheme)
    Grade Mark Description
    FNS   Fail No Submission
    F 1-49 Fail
    P 50-64 Pass
    C 65-74 Credit
    D 75-84 Distinction
    HD 85-100 High Distinction
    CN   Continuing
    NFE   No Formal Examination
    RP   Result Pending

    Further details of the grades/results can be obtained from Examinations.

    Grade Descriptors are available which provide a general guide to the standard of work that is expected at each grade level. More information at Assessment for Coursework Programs.

    Final results for this course will be made available through Access Adelaide.

  • Student Feedback

    The University places a high priority on approaches to learning and teaching that enhance the student experience. Feedback is sought from students in a variety of ways including on-going engagement with staff, the use of online discussion boards and the use of Student Experience of Learning and Teaching (SELT) surveys as well as GOS surveys and Program reviews.

    SELTs are an important source of information to inform individual teaching practice, decisions about teaching duties, and course and program curriculum design. They enable the University to assess how effectively its learning environments and teaching practices facilitate student engagement and learning outcomes. Under the current SELT Policy ( course SELTs are mandated and must be conducted at the conclusion of each term/semester/trimester for every course offering. Feedback on issues raised through course SELT surveys is made available to enrolled students through various resources (e.g. MyUni). In addition aggregated course SELT data is available.

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