STATS 3005 - Time Series III

North Terrace Campus - Semester 2 - 2019

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.

  • General Course Information
    Course Details
    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.
    Course Staff

    No information currently available.

    Course Timetable

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

  • Learning Outcomes
    Course Learning Outcomes
    1. 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
    All
  • Learning Resources
    Required Resources
    None, lecture notes are provided.
    Recommended Resources
    The 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 Learning
    This 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 Modes
    This 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.
    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 5 54
    Computing Practicals 6 12
    Total 156
    Learning Activities Summary
    Lecture 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. 

    Tutorial Outline

    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) 
    6. Forecasting. 

    Practical Outline

    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 Requirements
    None.
  • 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
    Component Weighting Outcomes Assessed
    Assignments 20% All
    Tutorial and practical participation 10% All
    Exam 70% All
    Assessment Related Requirements
    An aggregate score of at least 50% is required to pass the course.
    Assessment Detail
    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 Tutorials is awarded for attendance and participation in 5 out of 6 Tutorials.
    5% for Computing Practicals is awarded for attendance and participation in 5 out of 6 Practicals.
    Submission

    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.

    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.

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