Business Forecasting Using SAS: A Point-and-Click Approach

Duration: 3.0 days

Audience

This Level II course is designed for business analysts and others who want to create business forecasts using SAS/ETS software. Familiarity with business forecasting problems is recommended, but participants do not need forecasting or programming experience in order to benefit from this course.

Course Description

This course teaches business analysts how to carry out forecasting projects using the point-and-click interface in the Time Series Forecasting System in SAS/ETS software. Course topics include exponential smoothing models, autoregressive integrated moving average models, and dynamic regression models.

Prerequisites

Before attending this course you should be able to

create and access SAS data sets using the SAS windowing environment or a product such as SAS Enterprise Guide
use software to explore and analyze data, including the ability to create graphical displays of data
evaluate quantitative forecasts related to a business problem based on your advanced domain knowledge in an area such as finance, manufacturing, or retail.

A typical student will have college course work in a quantitative area, such as a basic business statistics course. Furthermore, a student should have experience using Microsoft Excel or a similar product for data summarization and presentation.

Course Contents

Business Forecasting

introduction to business forecasting
introduction to time series forecasting
measuring forecast accuracy
descriptive and exploratory analysis of time series data

Simple Forecast Models

modeling trend
modeling seasonality
using indicator variables to model events
exponential smoothing models with trend components
exponential smoothing models with trend and seasonal components

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 Forecast Models for Nonstationary Time Series

using differencing to model trend and seasonality
trend models
seasonal models
forecasting the airline data
forecasting the consumer electronics data

Forecast Models with Explanatory Variables

ordinary regression models
event models
time series regression models
event analysis using the seat belt law data
event analysis using the airline data
evaluating advertising effectiveness

Processing Time Series Data (Self-Study)

processing time-stamped data
augmenting data with event variables

Software Addressed

This course addresses the following software product(s): SAS/ETS. SAS/ETS software includes the Time Series Forecasting System.

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