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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. The aim is to learn the basics of the mathematical theory of linear time series and to acquire practical skills through computer implementation in real-world applications.
Course description Mathematical basics of Probability and Statistics for time series analysis: white noise, expectation, variance, auto-covariance, strong stationarity and second-order stationarity.
- Linear stationary time series: Moving average, Autoregressive and ARMA models.
- Second-order theory and Frequency analysis
- Introduction to GARCH and Stochastic Volatility models
- Introduction to State Space models and the Kalman filter.
- 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))
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 2021/22, 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. Use the theory and statistical tools of linear time series to analyse temporally structured data
  2. Use a range of likelihood based methods for statistical estimation and inference
  3. Use computer packages in R statistical language to fit time series models and produce forecasts in a broad range of applications including biology, finance and the environment
Reading List
Brockwell-Davis: Introduction to Time Series and Forecasting, 2nd Edition, Springer.

Shumway and Stoffer: Time Series Analysis and Its Applications (with R examples), 4th edition, Springer.
Priestley: Spectral Analysis and Time Series Vol 1. Academic Press.
Additional Information
Graduate Attributes and Skills Not entered
KeywordsTS,Time Series
Course organiserDr Ioannis Papastathopoulos
Tel: (0131 6)50 5020
Course secretaryMiss Gemma Aitchison
Tel: (0131 6)50 9268
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