
Academic Year:  2013/4 
Owning Department/School:  Department of Mathematical Sciences 
Credits:  6 
Level:  Masters UG & PG (FHEQ level 7) 
Period: 
Semester 2 
Assessment:  CW 25%, EX 75% 
Supplementary Assessment: 
MA50085 Mandatory extra work (where allowed by programme regulations) 
Requisites:  
Description:  Aims: To introduce a variety of statistical models for time series, cover the main methods for analysis and give practical experience in fitting such models. Learning Outcomes: 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 R * compute forecasts for a variety of linear methods and models. * demonstrate critical thinking and a deep understanding of some aspects of time series theory and application. Skills: Numeracy T/F A Problem Solving T/F A Written and Spoken Communication F A 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
