Postgraduate Course: Time Series Analysis and Forecasting (MATH11073)
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 | 5 |
ECTS Credits | 2.5 |
| Summary | Filtering, autoregressive and moving average models, ARMA and ARIMA models, forcasting, seasonal variation. |
| Course description |
week 6 - Introduction, white noise
week 7 - Moving average and autoregressive models
week 8 - ARMA and ARIMA models
week 9 - Forecasting
week 10 - Seasonal variation and other topics
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
| Not being delivered |
Learning Outcomes
On completion of this course, the student will be able to:
- The ability to demonstrate an understanding of the principles behind modern forecasting techniques.
- The ability to select and appropriate model, to fit parameter values, and to carry out the forecasting calculation.
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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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