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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Time Series Analysis (MATH11062)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits7.5 ECTS Credits3.75
SummaryThis 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.

This course is delivered by Heriot Watt University and is only available to students on the MSc Financial Mathematics programme.
Course description 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements MSc Financial Mathematics students only.
Course Delivery Information
Academic year 2017/18, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 75 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 41 )
Additional Information (Learning and Teaching) Examination takes place at Heriot-Watt University.
Assessment (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment) See 'Breakdown of Assessment Methods' and 'Additional Notes' above.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
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.
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.
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr David Siska
Tel: (0131 6)51 9091
Course secretaryMs Hannah Burley
Tel: (0131 6)50 4885
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