- ARCHIVE as at 1 September 2013 for reference only

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Time Series Analysis (MATH11062)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Course typeStandard AvailabilityNot available to visiting students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits7.5
Home subject areaMathematics Other subject areaFinancial Mathematics
Course website None Taught in Gaelic?No
Course descriptionThis half-course aims to provide student with an introduction to time series analysis, including models with applications in finance. Presenting the material in the form of a specific half-module allows for greater flexibility and makes it available to postgraduate students on other programmes who would benefit.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements MSc Financial Mathematics students only.
Additional Costs None
Course Delivery Information
Delivery period: 2013/14 Semester 2, Not available to visiting students (SS1) Learn enabled:  Yes Quota:  None
Web Timetable Web Timetable
Course Start Date 13/01/2014
Breakdown of Learning and Teaching activities (Further Info) Total Hours: 75 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Summative Assessment Hours 1.5, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 41 )
Additional Notes Examination takes place at Heriot-Watt University.
Breakdown of Assessment Methods (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
No Exam Information
Summary of Intended Learning Outcomes
On completion of this course the student should be able to:
- demonstrate knowledge of, and a critical understanding of, the main concepts of time series analysis
- demonstrate knowledge of, and a critical understanding of, the main properties of MA, AR, ARMA, ARIMA, and RW models
- use least squares, maximum likelihood and other methods to fit time series models to the data
- select proper model(s) using e.g. AIC or BIC
- fit trend and seasonal trend to the data, and fit time series models to the residuals
- understand methods used to produce forecasts
- understand ARCH, GARCH and other nonlinear time series models and their applications for modelling of financial data
- understand time series data well, and perform basic calculations and summaries of time series data
- understand and critically assess time series models fitted by computer packages
- use a range of time series models to produce forecasts
- communicate meaningfully and productively with others (including practitioners and professionals in the financial services industry) on time series analysis issues
- Demonstrate the ability to earn independently
- Manage time, work to deadlines and prioritise workloads.
Assessment Information
See 'Breakdown of Assessment Methods' and 'Additional Notes' above.
Special Arrangements
MSc Financial Mathematics students only.
Additional Information
Academic description Not entered
Syllabus White noise series, univariate stationary and integrated non-stationary random series.
Backwards shift operator, backwards difference operator, and the roots of the characteristic equation of a time series.
Define a time series through a general linear filter of another stationary random series (particularly of a white noise series).
Well known models for linear processes ¿ stationary autoregressive (AR), moving average (MA), autoregressive moving average (ARMA); nonstationary integrated ARMA(ARIMA).
Random walks with and without drift, particularly those with normally distributed increments.
A short introduction to multivariate time series models, in particular VAR model.
Cointegrated processes.
Estimation, diagnosis and identification of time series models.
Non-linear (e.g. TAR and GARCH), non-stationary (e.g. regression with stationary errors) time series models.
Applications of time series models and forecasts from time series data using Box-Jenkins method and extrapolation.
Smoothing techniques applied to time series and seasonal adjustment.
Transferable skills Not entered
Reading list Box, G.E. and Jenkins, G.M. (1976). Time Series Analysis: Forecasting and Control. Holden Day, San Francisco.
Brockwell, P.J. and Davis, R.A. (1991). Time Series: Theory and Methods. Springer, New York.
Diggle, P.J. (1990). Time Series ¿ A Biostatistical Introduction. Clarendon Press, Oxford.
Fuller, W.A. (1996). Introduction to Statistical Time Series. John Wiley, New York.
Hamilton (1994). Time Series Analysis (Chapters 11 and 17¿20). Princeton University Press.
Falk, E. et al. (2006). A First Course on Time Series Analysis¿Examples with SAS. See website.
Study Abroad Not Applicable.
Study Pattern See 'Breakdown of Learning and Teaching activities' above.
Course organiserDr Sotirios Sabanis
Tel: (0131 6)50 5084
Course secretaryMrs Kathryn Mcphail
Tel: (0131 6)50 4885
Help & Information
Search DPTs and Courses
Degree Programmes
Browse DPTs
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Important Information
© Copyright 2013 The University of Edinburgh - 10 October 2013 4:52 am