Department of Mathematical Sciences, Unit Catalogue 2009/10 |
MA30085: Time series |
 Credits:
| 6 |
| Level:
| Honours |
| Period: | Semester 2 |
| Assessment:
| EX 100% |
| Supplementary Assessment: | Like-for-like reassessment (where allowed by programme regulations) |
| Requisites:
| Before taking this unit you must take MA20035 |
| Description: | 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. 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. |