|
 Academic Year: |
2014/5 |
| Owning Department/School: |
Department of Mathematical Sciences |
| Credits: |
6 |
| Level: |
Honours (FHEQ level 6) |
| Period: |
Semester 2 |
| Assessment Summary: |
CW 25%, EX 75% |
| Assessment Detail: |
|
| Supplementary Assessment: |
MA30085 Mandatory Extra Work (where allowed by programme regulations) |
| Requisites: |
Before taking this unit you must take MA20227 |
| 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. Skills: Numeracy T/F A Problem Solving T/F A Written and Spoken Communication F 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: |
MA30085 is Optional on the following programmes:Programmes in Natural Sciences
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