MAS8382 : Time Series Data
- Offered for Year: 2019/20
- Module Leader(s): Dr Sarah Heaps
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
|Semester 1 Credit Value:||10|
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.
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.
|Guided Independent Study||Assessment preparation and completion||12||1:00||12:00||Background reading|
|Guided Independent Study||Assessment preparation and completion||12||1:00||12:00||Lecture follow-up|
|Scheduled Learning And Teaching Activities||Lecture||9||2:00||18:00||Lectures|
|Scheduled Learning And Teaching Activities||Practical||12||2:00||24:00||Computer Practicals|
|Guided Independent Study||Project work||1||22:00||22:00||Main project|
|Guided Independent Study||Project work||4||3:00||12:00||Practical 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.
The format of resits will be determined by the Board of Examiners
|Practical/lab report||2||M||40||4 separate reports, each max 250 words and counting 10%|
|Report||2||M||60||Main 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.