|
 Academic Year:
| 2012/3 |
| 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 state-space 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. State-space models: Dynamic linear models and the Kalman filter. |
| Programme availability: |
MA50085 is Optional on the following programmes:Department of Mathematical Sciences
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