STATS 3005 - Time Series III
North Terrace Campus - Semester 2 - 2014
-
General Course Information
Course Details
Course Code STATS 3005 Course Time Series III Coordinating Unit Statistics Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Prerequisites MATHS 1012 (Note: from 2015 the prerequisites for this course will be MATHS 2103 or (MATHS 1012 and ECON 2504) or (MATHS 2201 and MATHS 2202). Please plan your 2014 enrolment accordingly). Assumed Knowledge STATS 2107 Assessment ongoing assessment 30%, exam 70% Course Staff
Course Coordinator: Associate Professor Inge Koch
Course Timetable
The full timetable of all activities for this course can be accessed from .
-
Learning Outcomes
Course Learning Outcomes
1 Demonstrate understanding of the concepts of time series and its application finance and other areas.
2 Demonstrate familiarity with a range of examples for the different topics.
3 Understand the underlying concepts in the time series and frequency domain.
4 Apply ideas to real time series data and interpret outcomes of analyses.
5 Demonstrate skills in communicting mathematics orally and in writing.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) Knowledge and understanding of the content and techniques of a chosen discipline at advanced levels that are internationally recognised. 1,2,3,4,5 The ability to locate, analyse, evaluate and synthesise information from a wide variety of sources in a planned and timely manner. 2,4,5 An ability to apply effective, creative and innovative solutions, both independently and cooperatively, to current and future problems. 4,5 Skills of a high order in interpersonal understanding, teamwork and communication. 5 A proficiency in the appropriate use of contemporary technologies. 3,4 A commitment to continuous learning and the capacity to maintain intellectual curiosity throughout life. 1,2,3,4,5 A commitment to the highest standards of professional endeavour and the ability to take a leadership role in the community. 4,5 An awareness of ethical, social and cultural issues within a global context and their importance in the exercise of professional skills and responsibilities. 4,5 -
Learning Resources
Required Resources
None.Recommended Resources
Robert H. Shumway & David S. Stoffer, Time Series Analysis and Its Applications With R Examples (Second Edition), Springer (2006).
C. Chatfield, The Analysis of Time Series: Theory and Practice, Chapman and Hall (1975).
P.J. Brockwell and R.A. Davis, Time Series: Theory and Methods, Springer Series in Statistics (1986).Online Learning
This course uses MyUni exclusively for providing electronic resources, such as lecture notes, assignment papers, sample solutions, discussion boards, etc. It is recommended that students make appropriate use of these resources.
Link to MyUni login page:
-
Learning & Teaching Activities
Learning & Teaching Modes
This course relies on lectures as the primary delivery mechanism for the material. Tutorials supplement the lectures by providing exercises and example problems to enhance the understanding obtained through lectures. A sequence of written assignments provides assessment opportunities for students to gauge their progress and understanding.Workload
The information below is provided as a guide to assist students in engaging appropriately with the course requirements.
Activity Quantity Workload hours Lectures 24 72 Tutorials 6 18 Assignments 4 48 Practicals 6 18 Total 156 Learning Activities Summary
Lecture Outline
1. Notation, objectives of time series analysis: description, forecasting and understanding the mechanism generating a series
2. The basic notions of trend, serial dependence and stationarity
3. Measures of dependence, stationary time series
4. Estimation
5. Regression, exploratory data analysis and smoothing
6. MA models
7. AR and ARMA models
8. Difference Equations
9. Autocorrelation and Parital Autocorrelation
10. Forecasting and Durbin-Levinson algorithm
11. Estimation of parameters in forecasting
12. Integrated models for nonstationary data
13. Building ARIMA models
14. Multiplicative Seasonals ARIMA models
15. Spectral Analysis
16. Cyclic behaviour and peridicity
17. Spectral density
18. Periodogram and discrete Fourier transform
19. Parametric spectral estimation
20. Multiple series and cross-spectra
21. Linear filters
22. Lagged regression models, signal extraction and optimal filtering
23. Introduction to ARCH and GARCH modelling
Tutorial Outline
1. Covariance, weak and strong stationary processes
2. Moving average, differencing, and stationarity
3. AR and MA models
4. Stationarity, invertibility and prediction for ARMA models
5. ARMA model and the derivation of spectral density
6. Periodogram and spectral analysis
Practical Outline
1. Practical time series plot in R
2. Trend fitting and smoothing in R
3. AR and MA models, analyse time series data in R
4. Simulation of ARIMA models and model building in R
5. Explore the basic properties and use of the periodogram in R
6. Cumulative periodogram and fitting linear models in R -
Assessment
The University's policy on Assessment for Coursework Programs is based on the following four principles:
- Assessment must encourage and reinforce learning.
- Assessment must enable robust and fair judgements about student performance.
- Assessment practices must be fair and equitable to students and give them the opportunity to demonstrate what they have learned.
- Assessment must maintain academic standards.
Assessment Summary
Component Weighting Outcomes Assessed Assignments 16% All Individual Projects 4% All Test 20% All Exam 60% All Assessment Related Requirements
An aggregate score of at least 50% is required to pass the course.Assessment Detail
Test, Week 10: 20%Assessment Item Distributed Due Date Weighting Assignment 1 week 1 week 3 4% Assignment 2 week 4 week 6 4% Assignment 3 week 7 week 9 4% Assignment 4 week 10 week 12 4%
Individual projects throughout the semester: 4%Submission
All written assignments are to be submitted to the designated hand-in boxes within the School of Mathematical Sciences with a signed cover sheet attached.
Late assignments will not be accepted.
Assignments will have a two week turn-around time for feedback to students.
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 .
-
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 (http://www.adelaide.edu.au/policies/101/) 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.
-
Student Support
- Academic Integrity for Students
- Academic Support with Maths
- Academic Support with writing and study skills
- Careers Services
- Library Services for Students
- LinkedIn Learning
- Student Life Counselling Support - Personal counselling for issues affecting study
- Students with a Disability - Alternative academic arrangements
-
Policies & Guidelines
This section contains links to relevant assessment-related policies and guidelines - all university policies.
- Academic Credit Arrangements Policy
- Academic Integrity Policy
- Academic Progress by Coursework Students Policy
- Assessment for Coursework Programs Policy
- Copyright Compliance Policy
- Coursework Academic Programs Policy
- Intellectual Property Policy
- IT Acceptable Use and Security Policy
- Modified Arrangements for Coursework Assessment Policy
- Reasonable Adjustments to Learning, Teaching & Assessment for Students with a Disability Policy
- Student Experience of Learning and Teaching Policy
- Student Grievance Resolution Process
-
Fraud Awareness
Students are reminded that in order to maintain the academic integrity of all programs and courses, the university has a zero-tolerance approach to students offering money or significant value goods or services to any staff member who is involved in their teaching or assessment. Students offering lecturers or tutors or professional staff anything more than a small token of appreciation is totally unacceptable, in any circumstances. Staff members are obliged to report all such incidents to their supervisor/manager, who will refer them for action under the university's student鈥檚 disciplinary procedures.
The 成人大片 is committed to regular reviews of the courses and programs it offers to students. The 成人大片 therefore reserves the right to discontinue or vary programs and courses without notice. Please read the important information contained in the disclaimer.
