Postgraduate Course: Time Series (MATH11131)
|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
|Summary||The 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.
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)
|| Students MUST have passed:
Statistical Methodology (MATH10095)
||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 2023/24, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
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
|Assessment (Further Info)
|Additional Information (Assessment)
||Hours & Minutes
|Main Exam Diet S2 (April/May)||Time Series (MATH11131)||2:00|
On completion of this course, the student will be able to:
- Use the theory and statistical tools of linear time series to analyse temporally structured data
- Use a range of likelihood based methods for statistical estimation and inference
- 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
|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.
|Graduate Attributes and Skills
|Course organiser||Dr Ioannis Papastathopoulos
Tel: (0131 6)50 5020
|Course secretary||Miss Gemma Aitchison
Tel: (0131 6)50 9268