# MAS8382 : Time Series Data

• Offered for Year: 2024/25
• Module Leader(s): Dr Steffen Grunewalder
• Owning School: Mathematics, Statistics and Physics
• Teaching Location: Newcastle City Campus
##### Semesters

Your programme is made up of credits, the total differs on programme to programme.

 Semester 1 Credit Value: 10 ECTS Credits: 5.0 European Credit Transfer System

#### Aims

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 completion112:0012:00Formative exercise
Scheduled Learning And Teaching ActivitiesLecture62:0012:00Present in person lectures
Scheduled Learning And Teaching ActivitiesPractical62:0012:00In person practical
Guided Independent StudyProject work148:0048:00Main project
Scheduled Learning And Teaching ActivitiesDrop-in/surgery41:004:00In-person drop-in
Total100:00
##### Teaching Rationale And Relationship

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 online drop-ins provides opportunity for students to ask questions and receive immediate feedback.

#### 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

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

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.