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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2019/2020

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DRPS : Course Catalogue : School of Mathematics : Mathematics

Postgraduate Course: Time Series (MATH11131)

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 Credits10 ECTS Credits5
SummaryThe 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Statistical Methodology (MATH10095) OR ( Linear Statistical Modelling (MATH10005) AND Likelihood (MATH10004))
Co-requisites
Prohibited Combinations Other requirements MSc students should disregard the formal pre-requisites, however a good understanding of probability at undergraduate level is required. If in doubt, please consult with the Course Organiser.
Course Delivery Information
Academic year 2019/20, 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 )
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:
  1. demonstrate an understanding of the main concepts and statistical tools of linear time series theory
  2. use a range of likelihood based methods for statistical estimation
  3. demonstrate an understanding of nonlinear time series models including and their applications for modelling of financial data
  4. use computer packages to fit time series models, analyse temporally structured data and produce forecasts
Reading List
Brockwell-Davis: Introduction to Time Series and Forecasting, 2nd Edition, Springer, 2002
Additional Information
Graduate Attributes and Skills Not entered
KeywordsTS,Time Series
Contacts
Course organiserDr Ioannis Papastathopoulos
Tel: (0131 6)50 5020
Email: i.papastathopoulos@ed.ac.uk
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk
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