Postgraduate Course: Time Series (MATH11131)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | The course offers an introduction to the theory of time series analysis and forecasting. The aim is to learn the basics of the mathematical theory, to understand the wide applicability of the subject matter and to acquire practical skills through real-world applications. |
| Course description |
- Mathematical basics of statistical time series: white noise, expectation, variance, auto-covariance, stationarity.
- Linear stationary time series: Moving average, Autoregressive and ARMA models.
- Second-order theory and Frequency analysis
- Introduction to GARCH and Stochastic Volatility models
- The Kalman filter and State Space models.
- Applications for statistical modelling of biological, environmental and financial data
- Parameter estimation, likelihood based inference and forecasting with time series.
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Course Delivery Information
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| 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:
100
(
Lecture Hours 22,
Supervised Practical/Workshop/Studio Hours 5,
Summative Assessment Hours 2,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
69 )
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| Assessment (Further Info) |
Written Exam
90 %,
Coursework
10 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework: 10%
Examination: 90% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | Time Series (MATH11131) | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate an understanding of the main concepts and statistical tools of linear time series theory
- use a range of likelihood based methods for statistical estimation
- demonstrate an understanding of nonlinear time series models including and their applications for modelling of financial data
- use computer packages to fit time series models, analyse temporally structured data and produce forecasts
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Reading List
Brockwell-Davis: Introduction to Time Series and Forecasting, 2nd Edition, Springer, 2002
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | TS,Time Series |
Contacts
| Course organiser | Dr Ioannis Papastathopoulos
Tel: (0131 6)50 5020
Email: i.papastathopoulos@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
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