STATS 3005 - Time Series III
North Terrace Campus - Semester 2 - 2019
General Course Information
Course Code STATS 3005 Course Time Series III Coordinating Unit School of Mathematical Sciences Term Semester 2 Level Undergraduate Location/s North Terrace Campus Units 3 Contact Up to 3 hours per week Available for Study Abroad and Exchange Y Prerequisites STATS 2107 or (MATHS 1012 and ECON 2504) or (MATHS 2201 and 2202) Assumed Knowledge Experience with the statistical package R such as would be obtained from STATS 1005 or STATS 2107 Course Description Time series consist of values of a variable recorded in an order over a period of time. Such data arise in just about every area of science and the humanities, including econometrics and finance, engineering, medicine, genetics, sociology, environmental science. What makes time series data special is the presence of dependence between observations in a series, and the fact that usually only one observation is made at any given point in time. This means that standard statistical methods are not appropriate, and special methods for statistical analysis are needed. This course provides an introduction to time series analysis using current methodology and software.
Topics covered are: descriptive methods, plots, smoothing, differencing; the autocorrelation function, the correlogram and variogram, the periodogram; estimation and elimination of trend and seasonal components; stationary processes, modelling and forecasting with autoregressive moving average (ARMA) models; spectral analysis, the fast Fourier transform, periodogram averages and other smooth estimates of the spectrum; time-invariant linear filters; non-stationary and seasonal time series models; ARIMA processes, identification, estimation and diagnostic checking, forecasting, including extrapolation of polynomial trends, exponential smoothing, and the Box-Jenkins approach.
No information currently available.
The full timetable of all activities for this course can be accessed from Course Planner.
Course Learning Outcomes1. Demonstrate understanding of the concepts of time series and their application to health, climate, finance and other areas.
2. Demonstrate familiarity with a range of examples for the different topics covered in the course.
3. Understand the underlying concepts in the time series and frequency domains.
4. Apply ideas to real time series data and interpret outcomes of analyses.
5. Demonstrate skills in communicating mathematics and statistics, 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) Deep discipline knowledge
- informed and infused by cutting edge research, scaffolded throughout their program of studies
- acquired from personal interaction with research active educators, from year 1
- accredited or validated against national or international standards (for relevant programs)
All Critical thinking and problem solving
- steeped in research methods and rigor
- based on empirical evidence and the scientific approach to knowledge development
- demonstrated through appropriate and relevant assessment
All Teamwork and communication skills
- developed from, with, and via the SGDE
- honed through assessment and practice throughout the program of studies
- encouraged and valued in all aspects of learning
4,5 Career and leadership readiness
- technology savvy
- professional and, where relevant, fully accredited
- forward thinking and well informed
- tested and validated by work based experiences
2,4,5 Self-awareness and emotional intelligence
- a capacity for self-reflection and a willingness to engage in self-appraisal
- open to objective and constructive feedback from supervisors and peers
- able to negotiate difficult social situations, defuse conflict and engage positively in purposeful debate
Required ResourcesNone, lecture notes are provided.
Recommended ResourcesThe following books are useful references for the theory and applications of time series. I will refer to the book by Peter Diggle quite a lot.
P. Diggle. Time Series: A Biostatistical Introduction, Oxford Science Publications (1990).
C. Chatfield. The Analysis of Time Series, 7th Edition, CRC Press (2016).
P.J. Brockwell and R.A. Davis. Time Series: Theory and Methods, 2nd Edition, Springer Series in Statistics (1991).
Robert H. Shumway & David S. Stoffer. Time Series Analysis and Its Applications With R Examples, 3rd Edition, Springer (2016).
Online LearningThis course uses MyUni-Canvas for providing electronic resources, including the lecture notes, lecture recordings, assignment and tutorial materials, outlines solutions, datasets, practical sheets and so on. It is recommended that students make appropriate use of these resources. Please ensure you check MyUni regularly for any announcements, emails and discussions.
Learning & Teaching Activities
Learning & Teaching ModesThis course relies on lectures as the primary delivery mechanism for the material. Tutorials and practical classes supplement the lectures by providing exercises and problems to enhance the understanding obtained through lectures. A sequence of written assignments provides assessment opportunities for students to gauge their progress and understanding, and develop their analytical skills using R.
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 5 54 Computing Practicals 6 12 Total 156
Learning Activities SummaryLecture Outline
1. Examples, objectives of analysis, notation, stationarity
2. Smoothing, linear filters, moving average smoothers. serial correlation
3. Iterated smoothing, spline smoothing, autocorrelation and trend. Removing seasonality, decomposing a series, differencing
4. The autocoviance and autocorrletions functions
5. The sample autocorrelation function
6. Statistical properties of the sample autocovariance function. Mean ergodicity. Gaussian white noise
7. Tests for serial correlation. The variogram for unequally spaced data
8. Periodicity and the periodogram
9. The cumulative periodogram
10. Stationary random processes. The general linear process
11. The backward shift operator. The moving average model
12. The autoregressive process. Causality. The Yule-Walker equations
13. ARMA processes
14. Spectral analysis and the spectrum. Wold's Theorem
15. Spectral analysis, aliasing. Convergence of the spectra
16. Spectra for ARMA processes. Processes with continuous spectra
17. ARIMA models. Identification
18. The partial autocorrelation function
19. Identification of ARIMA models. The Akaike Informatio Criterion
20. Likelihood ratio tests. SARIMA models
21. Forecasting for ARMA processes
22. Minimum mean squared error prediction
23. Forecasting with SARIMA models, diagnostics and prediction.
1. Covariances for linear combinations of random variables, the autocovariance function
2. The periodogram
3. Autoregressive and moving average processes
4. The spectrum
5. Multivariate normal distributions and AR(1)
1. Creating, plotting and smoothing time series in R
2. Smoothing using polynomials, removing trend, and the acf
3. The periodogram
4. Interpreting the periodogram and cumulative periodogram; simulating AR, MA and ARMA processes
5. Simulating ARIMA processes, recognising stationarity and non-stationarity
6. Identifying ARIMA models, estimation, diagnostics and forecasting.
Specific Course RequirementsNone.
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.
Component Weighting Outcomes Assessed Assignments 20% All Tutorial and practical participation 10% All Exam 70% All
Assessment Related RequirementsAn aggregate score of at least 50% is required to pass the course.
Assessment Item Distributed Due Date Weighting Assignment 1 week 1 week 3 4% Assignment 2 week 3 week 5 4% Assignment 3 week 5 week 7 4% Assignment 4 week 7 week 9 4% Assignment 5 week 9 week 12 4% Tutorials Even weeks 5% Computing Practicals Odd weeks 5% Final exam 70%
5% for Computing Practicals is awarded for attendance and participation in 5 out of 6 Practicals.
All written assignments are to be submitted to the designated hand-in boxes on Level 6 of the School of Mathematical Sciences with a signed cover sheet attached.
Late assignments will not be accepted, unless accompanied by a medical certificate or arranged in advance with the Lecturer.
Assignments will have a two week turn-around time for feedback to students.
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
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