Time Series Analysis STAT 4321 – 2012
Time Series Analysis
STAT 4321 – Spring 2012
Syllabus
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, 3rd 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.