Time Series Analysis

STAT 4321  – Spring 2012

Syllabus

Final exam 2011     Solution 2011

Course Objectives:

·                     To understand time series analysis and forecasting.

·                     To gain some knowledge and skills on how to build ARIMA models.

·                     To highlight the impact of using R-software for data analysis.

·                     Determining how well the models fit the data.

·                     Develop the theory and methods of minimum mean square error forecasting for ARIMA models.

Course Outcomes:

Students will improve their knowledge and awareness on the following:

– Understand the basic concepts of Time Series.

– Model-building strategy for ARIMA modeling.

– Students will be able to mix application and theory throughout this course

– Use time series for applications from the real life.

– Ethics and social responsibilities.

Textbook:

Jonathan D. Cryer and Kung-Sik Chan (2008). Time Series Analysis With Applications in R, Second Edition, Springer.

References:

1.                  Box, G.E.P., Jenkins, G.M. and Reinsel, G.C. (1994). Time Series Analysis: Forecasting and Control, 3^rd Edition, Prentice Hall, New Jersey.

2.                  Brockwell, P.J. and Davis, R.A. (1996), Introduction to Time Series and Forecasting. Springer-Verlag, New York.

3.                  Brockwell P. J., and Davis R. A. Time Series: Theory and Methods. Second edition. New York: Springer-Verlag, 1991.

4.                  Chatfield, C. (2003). The Analysis of Time Series: An Introduction, 6th edition, Chapman and Hall, New York.

5.                  Hamilton J. D. (1994).  Time Series Analysis. Princeton University Press, Princeton.

6.                  Pole, A.,West, M. and Harrison, P. J. (1994); Applied Bayesian Forecasting and Time

7.                  Series Analysis. Chapman and Hall.