Department of Mathematical Sciences, Unit Catalogue 2011/12 

Credits:  6 
Level:  Masters UG & PG (FHEQ level 7) 
Period: 
Semester 2 
Assessment:  CW 25%, EX 75% 
Supplementary Assessment:  Likeforlike reassessment (where allowed by programme regulations) 
Requisites:  
Description:  Aims & Learning Objectives: Aims: To introduce a variety of statistical models for time series and cover the main methods for analysing these models. To facilitate an indepth understanding of the topic. Objectives: At the end of the course, the student should be able to: * Compute and interpret a correlogram and a sample spectrum; * derive the properties of ARIMA and statespace models; * choose an appropriate ARIMA model for a given set of data and fit the model using an appropriate package; * compute forecasts for a variety of linear methods and models; * demonstrate an indepth understanding of the topic. Content: Introduction: Examples, simple descriptive techniques, trend, seasonality, the correlogram. Probability models for time series: Stationarity; moving average (MA), autoregressive (AR), ARMA and ARIMA models. Estimating the autocorrelation function and fitting ARIMA models. Forecasting: Exponential smoothing, Forecasting from ARIMA models. Stationary processes in the frequency domain: The spectral density function, the periodogram, spectral analysis. Statespace models: Dynamic linear models and the Kalman filter. 
Programme availability: 
MA50085 is Optional on the following programmes:Department of Mathematical Sciences
