Applied Time Series Analysis

Course No.: SMI1132001     Credit(s):3

Course Description

This course introduces the theory and practice of time series analysis, with an emphasis on practical skills. Having completed this course, you will be able to model and forecast a time series as well as read papers from the literature and start to do original research in time series analysis. More generally, you will acquire an appreciation for the role of dependence in statistical modeling.

Course Learning Outcomes

On completion of the course, students should be able to:

lIdentify and understand the structure of multivariate data and be able to phrase the appropriate scientific questions in terms of parameters of interest.

lUnderstand the various assumptions needed for the various methodologies covered in the class as well as their implementation.

lImplement analyses of these methods in a statistical software package.

lRead the scientific literature and comprehend the use (and misuse) of multivariate analysis methodologies reported by study authors.

Relationship to Other Courses

Pre-requisites: Statistics, Linear Algebra, Probability Theory, Mathematical Statistics.

Textbook and Reading Lists

Textbook: 

Jianping Zhu, Applied Multivariate Statistical Analysis. Science Press, Beijing, 2009.

Suggested reading lists:

R.A. Johnson and D.W. Wichern, Applied Multivariate Statistical Analysis (6th edition). Prentice Hall, New York, 2007.

C. R. Rao, Linear Statistical Inference and its Applications. Wiley, 2000.

T. W. Anderson, An Introduction to Multivariate Statistical Analysis. Wiley, 2006.

Course Assessment

Course Schedule