Postgraduate Course: Time Series Analysis (MATH11062)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 7.5 |
| Home subject area | Mathematics |
Other subject area | Financial Mathematics |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | This 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
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| Delivery period: 2014/15 Semester 2, Not available to visiting students (SS1)
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Learn enabled: No |
Quota: None |
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Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| 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 )
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| Additional Notes |
Examination takes place at Heriot-Watt University.
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| 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.
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| 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. http://statistik.mathematik.uni-wuerzburg.de/timeseries/download/versions/2006-February-01-times.pdf
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| Study Abroad |
Not Applicable. |
| Study Pattern |
See 'Breakdown of Learning and Teaching activities' above. |
| Keywords | TSA |
Contacts
| Course organiser | Dr Sotirios Sabanis
Tel: (0131 6)50 5084
Email: |
Course secretary | Dr Jenna Mann
Tel: (0131 6)50 4885
Email: |
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