Stat 350 syllabus
Applied Time Series Analysis
Bulletin Description Fundamentals concepts; classical regression models as
forecasting models, exponential smoothings, stationary and nonstationary
models, additive and multiplicative decompositions, moving average,
autoregressive, ARMA and ARIMA processes, estimation in MA, AR, ARMA and
ARIMA processes. Box-Jenkins methodology, computer aided modeling,
Prerequisite ST 310 or ST 315 or ST 320 or ST 335. Computer Lab fee.
Text Time Series Forecasting: Unified Concepts and Computer Implementation,
by Bruce L. Bowerman and Richard T. O'Connell, 3rd edition, Duxbury Press, 1991.
Coverage Material to be selected by the instructor
Learning Objectives This course is designed to give applied yet sophisticated
presentation of classical and modern statistical techniques that are useful in short
term forecasting of time series data. The development of topics begins with the
basic simple linear regression and moves into complex regression based
prediction using dummy variables and smoothing methods. Additive and
multiplicative decompositions, based on four major aspects of a time series data
are discussed and finally the modern technique, known as Box-Jenkins
methodology is introduced where the nature of time series is identified using the
concept of auto correlation and partial auto correlation. Estimation of parameters
is discussed. Statistical computer software is intended to enhance the facility with
applications of various techniques covered in this course.
Last Updated February 18, 2014