|Delivery Type||Delivery length / details|
|Lecture||22 x 1 Hour Lectures|
|Assessment Type||Assessment length / details||Proportion|
|Semester Exam||2 Hours conventional examination||100%|
|Supplementary Exam||2 Hours supplentary examination||100%|
On completion of this module, students should be able to:
1. understand the ideas of autocorrelation;
2. calculate autocovariances and autocorrelations for linear time series models;
3. identify suitable models for different data sets;
4. use models to forecast future values and set confidence limits on them.
To introduce students to the vast area of Time Series Analysis and Forecasting as a branch of statistical methodology.
Time Series Analysis has, over the past 30 years, been one of the fastest growing areas of Statistics. It is concerned with situations where data or random variables are generated sequentially through time, and this makes the variables involved dependent on one another as opposed to having independent variables as in most other Statistics problems. This module develops a class of models to cater for such dependence, and considers how they are fitted to data, as well as how they may be used to forecast future values beyond the data set.
Historical background; theoretical properties of time series; the ideas of stationarity and invertibility; backward shift and difference operators
Linear Time Series Models
General linear filters. Autoregressive, Moving Average and mixed models. The ARMA family. Techniques for evaluating autocorrelation and partial autocorrelation functions. Aggregation and the case for ARMA models. Non-stationarity and ARIMA models.
Identification, estimation and diagnostic checking as an iterative process. Sample autocorrelations. Least squares and conditional least squares. Differencing to achieve stationarity.
Minimum mean squared error. The Fundamental Theorem of Forecasting. Forecast error variances.
|Skills Type||Skills details|
|Application of Number||Throughout the module|
|Communication||Written worksheet solutions.|
|Improving own Learning and Performance||Feedback via tutorials|
|Information Technology||Interpretation of specialist computer output.|
|Personal Development and Career planning||Students exposed to an area of Statistics that has wide applicability.|
|Problem solving||Problem solving is central to the development of time series models, and to the ultimate goal of producing accurate forecasts of future values.|
|Research skills||Students encouraged to consult relevant literature and compare various methods.|
|Subject Specific Skills||General modeling ability.|
This module is at CQFW Level 6