COURSE OUTLINE 

 Introduction to Time Series Modeling
 Autocorrelation Function and Spectral Properties of Stationary Processes
 Linear Stationary Models – AR, MA, ARMA
 Linear Nonstationary Models – IMA, ARIMA
 Forecasting: Minimum Mean Square Forecast, ARMA, ARIMA models, State Space Models, Kalman Filtering
 Model Identification – Autocorrelation, Partial Autocorrelation Functions, Initial Estimates, unit root test
 Model Estimation – Likelihood Function, Least Squares Estimates
 Model Diagnostic Checking
 Seasonal Models
 Transfer Function Models – Linear Transfer Function, Discrete Dynamic Models
 Aspects of Process Control – Process Monitoring, Feedback Control, MMSE Control

Text: 
Time Series Analysis, by G. E. P. Box, G. M. Jenkins and G. C. Reinsel, Prentice Hall, 3rd Edition, 1994.
References: Introduction to Time Series and Forecasting, by P. J. Brockwell and R. A. Davis, 2nd Edition, Springer 2002.
Time Series: Theory and Methods, by P. J. Brockwell and R. A. Davis, Springer, 2nd Edition, 1991.
Introduction to Statistical Time Series, by W. A. Fuller, 2nd Edition, Wiley and Sons, 1996.

