720—Time Series Analysis. (3) (Prereq: STAT 704 and 512) Stochastic properties, identification, estimation, and forecasting methods for stationary and nonstationary time series models.

Usually Offered: Odd Numbered Springs

Purpose: To acquaint graduate students from various disciplines with a firm understanding of the ARIMA(p,d,q) class of models and to use this information to fit appropriate models to real data and forecast if desired; to familiarize students with the frequency domain approach including the definition, interpretation and estimation of the spectral density.

Current Textbook: 
Required: Introduction to Time Series and Forecasting, 2nd edition, by P.J. Brockwell and R.A. Davis, Springer, 2002. 
Software: ITSM 2000, comes free with the textbook. 
Recommended: Time Series: Theory and Methods, 2nd edition, P.J. Brockwell and R.A. Davis, Springer, 1991.

 

Topics Covered	Chapters	Week(s)
Introduction to time series	1	1
Time series models, trend and seasonal component	1	1
Stationary processes	2	1.5
ARMA models, ACF, PACF	3	1.5
Spectral Analysis	4	1
Modeling and forecasting with ARMA models	5	1.5
Nonstationary and seasonal time series models. Unit roots	6	1
Multivariate time series. VAR and VEC models. Cointegration and Granger causality	7	1
Forecasting techniques: ARAR, Holt-Winters and seasonal Holt-Winter	9	1
ARCH and GARCH models	10	1
Transfer function models. Intervention analysis and state-space models	8, 10, Notes	1
Recent developments in time series analysis and forecasting	Notes	1

The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.  

Contact Faculty: TBD