Department of Mathematical Sciences, Unit Catalogue 2008/09
MA30085 Time series
| Credits: 6 |
| Semester: 2|
|Assessment: EX 100%|
|Before taking this unit you must take MA20035|
Aims & Learning Objectives:
Aims: To introduce a variety of statistical models for time series and cover the main methods for analysing these models.
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 state-space 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.
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.