- Class Number 4003
- Term Code 3330
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Xuan Liang
- Dr Xuan Liang
- Class Dates
- Class Start Date 20/02/2023
- Class End Date 26/05/2023
- Census Date 31/03/2023
- Last Date to Enrol 27/02/2023
This course considers statistical techniques to evaluate processes occurring through time. It introduces students to time series methods and the applications of these methods to different types of data in various contexts (such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, risk management, and sociology). Time series modelling techniques will be considered with reference to their use in forecasting where suitable. While linear models will be examined in some detail, extensions to non-linear models will also be considered.
The topics will include: deterministic models; linear time series models, stationary models, homogeneous non-stationary models; the Box-Jenkins approach; intervention models; non-linear models; time-series regression; time-series smoothing; case studies. Statistical software R will be used throughout this course.
Heavy emphasis will be given to fundamental concepts and applied work. Since this is a course on applying time series techniques, different examples will be considered whenever appropriate.
Upon successful completion, students will have the knowledge and skills to:
- Apply the concept of stationarity to the analysis of time series data in various contexts (such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, and sociology);
- Run and interpret time-series models and regression models for time series;
- Use the Box-Jenkins approach to model and forecast time-series data empirically;
- Use multivariate time-series models such as vector autoregression (VAR) to analyse time series data;
- Utilise fundamental research skills (such as data collection, data processing, and model estimation and interpretation) in applied time series analysis; and,
- Use existing R function and packages for analysing time series data, and develop R code where appropriate.
Where possible, topics will be related to current research problems and reflect real-world situations to emphasize the use of the techniques covered.
Additional Course Costs
The only other additional course costs are a calculator, textbook (if purchased) and printing materials.
Examination Material or equipment
There is no final examination for this course. Please see Assessment sections for details and required material.
The course does not have a required textbook.
Shumway, R. H. and Stoffer, D. S. Time Series Analysis and its Application, Springer.
A free ebook copy of the textbook is available at: https://www.researchgate.net/publication/265365840_Time_Series_Analysis_and_Its_Applications_With_R_Examples
Students will be given feedback (through both verbal and written comments) in the following forms in this course:
• To the whole class during lectures.
• Within tutorials.
• Individually during consultation hours.
Students will also be given written comments in the marked assignments.
ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.
As a further academic integrity control, students may be selected for a 15-minute individual oral examination of their written assessment submissions. Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule.
Support for Students
The University offers a number of support services for students. Information on these is available online from http://students.anu.edu.au/studentlife/
Communication via Email
If I, or anyone in the School, College or University administration, need to contact you, we will do so via your official ANU student email address, which you need to check regularly. If you have any questions for the teaching and course convenor make sure you email them using your ANU email address. Emails from personal email accounts will not be answered.
Students are expected to check the Wattle site for announcements about this course, e.g. changes to timetables or notifications of cancellations.
Your final mark for the course will be based on the raw marks allocated for each of your assessment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.
In assignments and exams, students must appropriately reference any results, words or ideas that they take from another source which is not their own. A guide can be found at https://academicskills.anu.edu.au/resources/handouts/referencing-basics.
STAT8002 shares the same lecture content and assignments as STAT4102.The different cohorts of students will also be treated separately in grading and any scaling that is applied.
|Week/Session||Summary of Activities||Assessment|
|1||Course Introduction; Introduction and Characteristics of Time Series|
|2||Estimation of Serial Correlation|
|3||Modelling Stationary Time Series – AR Model|
|4||Estimation of AR Models|
|5||Modelling Stationary Time Series – MA Model||Submission of Assignment 1|
|6||Estimation of MA Model|
|7||Modelling Stationary Time Series – ARMA Model and Estimation|
|9||Building ARMA Models and Diagnostics|
|10||Modelling Non-Stationary Time Series – Time Trend Model||Submission of Assignment 2|
|11||Modelling Non-Stationary Time Series – ARIMA Model|
|12||Seasonal Models; Introduction to Multiple Time Series Analysis - Vector Autoregressive (VAR) Model|
Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. Students should enrol in their tutorial using MyTimetable.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||15 %||22/03/2023||31/03/2023||1,2,3|
|Assignment 2||25 %||10/05/2023||19/05/2023||1,2,3,4,5,6|
|Final Project||60 %||01/06/2023||29/06/2023||1,2,3,4,5,6|
* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details
ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:
- Academic Integrity Policy and Procedure
- Student Assessment (Coursework) Policy and Procedure
- Special Assessment Consideration Guideline and General Information
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
- Code of practice for teaching and learning
The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.
Moderation of Assessment
Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.
Course content delivery will take the form of weekly on-campus lectures (recorded and available via echo360 on Wattle) and weekly tutorials, delivered in hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos). Weekly consultations with the lecturer and the tutor will be conducted over Zoom.
There is no examination for this course.
Assessment Task 1
Learning Outcomes: 1,2,3
Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials from Week 1 to 4. Assignments will include derivation problems and data analysis with R. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in Week 3 during lectures and on Wattle. Assignments are expected to be in a PDF or Word file.
Value: 15% and compulsory.
Estimated return date: A week after submission.
Assessment Task 2
Learning Outcomes: 1,2,3,4,5,6
Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials from Week 1 to 9. Assignments will include derivation problems and data analysis with R. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in Week 8 during lectures and on Wattle. Assignments are expected to be in a PDF or Word file.
Value: 25% and compulsory.
Estimated return date: A week after submission.
Assessment Task 3
Learning Outcomes: 1,2,3,4,5,6
Turnitin submission. The students are expected to complete this project individually. This final project will be based on all the materials covered throughout the whole semester. The final project is a compulsory piece of assessment and worth 60% of the final raw score. Students will be provided with further details regarding the final project 3 weeks before the due date. This project requires the use of R to analyse real data. Written reports for this project (10 pages maximum for the main manuscript and 20 pages maximum for the appendix based on the format below, and all the R codes should be relegated to the appendix) are expected to be submitted via Turnitin.
Value: 60% and compulsory.
Report Format – PDF or Word file
Use Australian English spelling. All pages (uploaded in PDF or Word form) must be as follows:
• Black type, or occasional coloured type for highlighting purposes;
• Single column;
• White A4 size paper with at least 0.5 cm margin on each side, top and bottom;
• Text must be size 12 point Times New Roman or an equivalent size before converting to PDF format and must be legible to assessors;
• References and appendices only can be in 10 point Times New Roman or equivalent.
Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.
The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.
The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.
The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.
Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule. You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.
There is no hardcopy submission in the course.
No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.
The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.
The marked assignments will be returned online.
Extensions and Penalties
Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.
Resubmission of Assignments
It will not be possible for assignments to be resubmitted.
Distribution of grades policy
Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.
Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.
Support for students
The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).
- ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
- ANU Access and inclusion for students with a disability or ongoing or chronic illness
- ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
- ANU Academic Skills and Learning Centre supports you make your own decisions about how you learn and manage your workload.
- ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
- ANUSA supports and represents undergraduate and ANU College students
- PARSA supports and represents postgraduate and research students
Spatial modeling, panel data, social network data modeling, biostatistics, environmental statistics and time series.
Dr Xuan Liang
Dr Xuan Liang