Requirements/prerequisites:
Basic Statistics and Mathematics
Duration: 2 terms, 11 weeks per term
Number of lectures per week: 2 lectures plus 1 laboratory hour per week.
Assessment:
Compulsory module assignments, worth
40% of the final mark.
The
assignment marks will be divided equally, with 20% from each part.
End-of-year Examination: 3 hour examination worth 60%. The examination paper will consist of two sections corresponding to the two parts, with 3 questions per section. Students will be required to complete 2 questions from each section.
Description:
This description is from the description for 2008-09 (when the module was called ST370). Some changes can be expected for 2009-10.
Aims
This module is divided into two parts
â- Applied Forecasting (AF)
and Multivariate Linear Analysis (MLA). Each part runs for 12 weeks.
In the first semester (Dr. Rozenn Dahyot)
several methods of forecasting will be examined,
including exponential smoothing and its Holt-Winters extension,
auto-regression, moving average, and further regression based methods
that take into account seasonal trends of lagged variables. The module
will be practical, and will involve every student in extensive analysis
of case study material for a variety of time series data.
In the second semester (Dr. Brett Houlding) classical multivariate techniques of discriminant analysis, principal component analysis, clustering and logistic regression are examined. There is a strong emphasis on the use and interpretation of these techniques. More modern techniques, some of which address the same issues, are covered in the SS module Data Mining.
Learning Outcomes
When students have successfully completed this module
they should be able to:
Define and describe the different patterns that can be found in times series and propose the methods that can be used for their analysis.
Syllabus
Exploratory Data Analysis;
Classical multivariate techniques: discriminant analysis, principal component analysis, clustering and logistic regression;
Introduction to forecasting; auto-regressive models, data transformations, seasonality, exponential smoothing, performance measures.
Use of transformations and differences.
Textbooks:
Jul 9, 2009