Forecasting Using SAS Software: A Programming Approach

Duration: 3.0 days

This course teaches analysts how to use SAS/ETS software to create forecasting models, evaluate the model for accuracy, and forecast future values using the model.

Learn how to

• build simple forecast models
• build advanced forecast models for autocorrelated time series and for time series with trend and seasonality
• build forecast models that contain explanatory variables.

Who should attend: Scientists, engineers, and business analysts who have the responsibility of forecasting for their organizations

Prerequisites

Before attending this course, you should have experience using SAS to enter or transfer data and to perform elementary analyses, such as computing row and column totals and averages, and producing charts and plots. You can gain this experience by completing the SAS Programming 1: Essentials and SAS Programming 2: Data Manipulation Techniques courses. Knowledge of SAS Macro language programming is useful, but not required. A student with no experience in data analysis and statistical modeling can gain the prerequisite knowledge by completing the Statistics II: ANOVA and Regression course.

Course Contents

Introduction to Forecasting

• introduction to SAS Time Series Forecasting software
• introduction to statistical time series forecasting
• measuring goodness-of-fit and accuracy

Simple Forecast Models

• modeling trend
• modeling seasonality
• using pulse, step, and ramp variables to model events

Advanced Forecast Models for Stationary Time Series

• introduction to Box-Jenkins forecasting
• autoregressive models
• moving average models
• mixed autoregressive moving average models
• identifying an appropriate autoregressive moving average model
• estimation and forecasting methods

Advanced Models for Nonstationary Time Series

• the use of differencing to model trend and seasonality
• trend models
• seasonal models
• forecasting the airline passengers data
• forecasting with nonstandard date variables

Forecast Models with Explanatory Variables

• ordinary regression models
• event models
• time series regression models
• unobserved component models

Software Addressed

This course addresses the following software product(s): SAS/ETS.

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