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MAS8382 : Time Series Data

  • Offered for Year: 2022/23
  • Module Leader(s): Dr Joe Matthews
  • Owning School: Mathematics, Statistics and Physics
  • Teaching Location: Newcastle City Campus
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
Structured Guided LearningLecture materials91:3013:30Non-synchronous online pre-recorded lectures and set reading
Guided Independent StudyAssessment preparation and completion110:0010:00Formative and summative reports
Scheduled Learning And Teaching ActivitiesPractical62:0012:00Present in person, structured synchronous practical
Guided Independent StudyProject work147:0047:00Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery41:004:00Present in person drop-in
Guided Independent StudyIndependent study91:3013:30Lecture follow up/background reading
Teaching Rationale And Relationship

Pre-recorded lectures and set reading are used for the delivery of theory and explanation of methods, illustrated with examples. Practicals are used both for solution of problems and work requiring extensive computation and to give insight into the ideas/methods studied. There are two present-in-person practical sessions per week to ensure rapid feedback on understanding. Scheduled present-in-person drop-ins provides opportunity to ask questions and receive immediate feedback. Students unable to attend PiP will be able to complete the practical work at home and will be able to receive immediate feedback through joining the drop-ins virtually.

Alternatives as described will be offered to students unable to be present-in-person due to the prevailing C-19 circumstances.

Student’s should consult their individual timetable for up-to-date delivery information.

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Report1M100Main module project 2000 words
Zero Weighted Pass/Fail Assessments
Description When Set Comment
Oral PresentationMA 3 min video articulating the main findings of one aspect of the report
Formative Assessments
Description Semester When Set Comment
Practical/lab report1MA compulsory report allowing students to develop problem solving techniques, to practise the methods learnt and to assess progress.
Assessment Rationale And Relationship

A compulsory formative practical report allows the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback, before the summative assessments.

The oral presentation encourages students to focus on interpretation of statistical results, builds their skills in the presentation of statistical concepts, and provides opportunity for feedback.

In a foundational subject like the Mathematical Sciences, there is research evidence to suggest that continual consolidation of learning is essential and the fewer pieces of assessment there are, the more difficult it is to facilitate this. On this module, it is particularly important that the material on the earlier summative assessment is fully consolidated, before the later assessment is attempted.

Reading Lists