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

Duration: 3.0 days Course fee: $1,725 EPTO units: 3.3 CEUs: 1.8

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. Familiarity with business forecasting problems is recommended, but participants do not need forecasting or programming experience to benefit from this course.

Learn how to

identify trend and seasonality in time series data
propose simple trend, advanced trend, and/or seasonal models and fit these models to data
identify input variables to include in a time series regression model
identify the relationship between the target variable and input variables
propose and fit dynamic time series regression models to data.

Who should attend

Forecasters and business analysts

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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

This course addresses SAS/ETS. SAS/ETS software includes the Time Series Forecasting System.

Course Materials

Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

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