|Owning Department/School:||Department of Mathematical Sciences|
|Level:||Honours (FHEQ level 6)|
|Assessment:||EX 75%, CW 25%|
|Supplementary Assessment:||MA30085 Mandatory Extra Work (where allowed by programme regulations)|
|Requisites:||Before taking this unit you must take MA20227|
To introduce a variety of statistical models for time series, cover the main methods for analysis and give practical experience in fitting such models.
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.
Numeracy T/F A
Problem Solving T/F A
Written and Spoken Communication F
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.
MA30085 is Optional on the following programmes:Programmes in Natural Sciences