STATS 786 : Special Topic in Statistical Computing


2021 Semester One (1213) (15 POINTS)

Course Prescription

No prescription

Course Overview

Time series data arise in various areas such as agriculture, crime, demography, health, meteorology, economics, sales are a few among others. The analysis of these observed data at different time points lead to unique problems in statistical modelling and inference. This course provides a basic understanding of time series visualization, decomposition, regression, exponential smoothing methods, (seasonal) ARIMA models, dynamic regression models, model selection, and validation. Students get the opportunity to enhance their analytical and computer skills with exercises using R.

Course Requirements

No pre-requisites or restrictions

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Graduate Profile: Master of Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Use appropriate data visualizations to identify features present in time series. (Capability 1, 2, 3 and 4)
  2. Identify the most appropriate time series models for a given problem. (Capability 1, 2, 3 and 4)
  3. Fit commonly used linear/nonlinear regression models, exponential smoothing methods, (seasonal) ARIMA models, X13, and dynamic regression models using R. (Capability 1, 2, 3 and 4)
  4. Interpret and understand the software output for a given time series model. (Capability 1, 2, 3 and 4)
  5. Perform model selection and cross-validation. (Capability 1, 2, 3 and 4)


Assessment Type Percentage Classification
Worksheets 20% Individual Coursework
Test 10% Individual Test
Group project 20% Group Coursework
Final Exam 50% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5
Group project
Final Exam

Special Requirements

The mid-semester online test will be held in the evening. The date and time will be advised on Canvas at the beginning of the semester.

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 24 hours of lectures, 12 hours of tutorial, 29 hours of reading and thinking about the content, and 85 hours of work on assignments and/or test/exam preparation.

Delivery Mode

Campus Experience

The course is available for remote students.

Attendance is expected at scheduled activities including tutorials to complete components of the course.
Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings.
The course will not include live online events including tutorials.
Attendance on campus is required for the exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

  • R. H. Hyndman and G. Athanasopoulos. Forecasting: Principles and practice.
  • R. H. Shumway and D. S. Stoffer. Time series analysis and its applications: With R examples.
  • P. J. Brockwell and R. A. Davis. Introduction to time series and forecasting.
  • R. J. Hyndman, A. B. Koehler, J. K. Ord and R. D. Snyder. Forecasting with exponential smoothing: The state space approach.

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Digital Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

  • Level 1: Delivered normally as specified in delivery mode
  • Level 2: You will not be required to attend in person. All teaching and assessment will have a remote option. The following activities will also have an on-campus option: Lectures, tutorials, office hours
  • Level 3 / 4: All teaching activities and assessments are delivered remotely

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 22/02/2021 12:19 a.m.