MAS8382 : Time Series Data

Semester 1 Credit Value: 10
ECTS Credits: 5.0


To gain an understanding of the principles of time series analysis and to develop skills useful for the modelling, analysis and forecasting of time series.

Module Summary

A time series is a set of data ordered with respect to time, such as the sales of a product recorded each month or air temperature at a specific place measured at noon each day. In other branches of statistics, data are often regarded as independent draws from a population. In time series analysis we typically do not regard consecutive observations to be independent, and build special models to represent this dependence. Time series can also exhibit features such as trends and seasonal, or periodic, effects. In this module we look at modelling and inference for time series and how to produce forecasts for future observations.

Outline Of Syllabus

Introduction to time series, including trend effects and seasonality. Linear Gaussian processes, stationarity, autocovariance and autocorrelation. Autoregressive (AR), moving average (MA) and mixed (ARMA) models for stationary processes. Likelihood in a simple case such as AR(1). ARIMA processes, differencing, seasonal ARIMA as models for non-stationary processes. The role of sample autocorrelation, partial autocorrelation and correlograms in model choice. Inference for model parameters. Forecasting. Dynamic linear models and the Kalman filter. Use of R for time series analysis.

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion121:0012:00Background reading
Guided Independent StudyAssessment preparation and completion121:0012:00Lecture follow-up
Scheduled Learning And Teaching ActivitiesLecture92:0018:00Lectures
Scheduled Learning And Teaching ActivitiesPractical122:0024:00Computer Practicals
Guided Independent StudyProject work122:0022:00Main project
Guided Independent StudyProject work43:0012:00Practical write-ups
Teaching Rationale And Relationship

Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. A large number of practicals are scheduled in order to provide sufficient hands-on training and rapid feedback on understanding.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Practical/lab report2M404 separate reports, each max 250 words and counting 10%
Report2M60Main module project (max 1,500 words)
Assessment Rationale And Relationship

Written assignments (approximately 4 pieces of work of approximately equal weight) followed by a larger piece of project work allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; the smaller pieces of work are thus formative as well as summative assessment.

Reading Lists