Modeling Trend, Cycles, and Seasonality in Time Series Data Using PROC UCM

Duration: 1 half-day session CEU: 0.3

Presented by Rajesh Selukar, Ph.D., research statistician at SAS Institute

This lecture teaches students how to model, interpret, and predict time series data using UCMs. The UCM procedure analyzes and forecasts equally spaced univariate time series data using the Unobserved Components Models (UCM).

Learn how to

analyze time series data using a novel class of models called the Unobserved Componenet Models (UCM)
use the UCM procedure to find a suitable model for the series of interest, to obtain extensive model diagnostics, and to generate series forecasts and the forecasts of the constituent components
get detailed understanding of the series dynamics by analyzing the plots of the estimated components.

Who should attend

Those who want to analyze time series data to uncover patterns such as trend, seasonal effects, and cycles using the latest techniques

Course Materials

Students attend Live Web classes using a Web browser and a telephone and interact with their instructor and fellow classmates in real time. Each student receives an e-mail with instructions on how to join the class three business days before the class begins. The instructions e-mail includes a link to download the course materials, including the exercise files. Students need to download and print the course materials prior to class.

System Requirements

For Live Web, you must

review and meet the general system requirements.

U.S. Schedule

15DEC2008 Live Web,

Check for additional and updated schedule information online at support.sas.com/courses/lwbars.html.