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Forecasting Using SAS Software: A Programming Approach - FETSP

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.

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Duration:

3 days - Classroom

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 organisations

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 learner with no experience in data analysis and statistical modeling can gain the prerequisite knowledge by completing the Introduction to Statistics Using SAS®: ANOVA, Linear Regression and Logistic Regression course.

Course Contents

Introduction to Forecasting

  • time series and forecasting
  • introduction to SAS forecasting software
  • measuring goodness-of-fit and accuracy

Forecast Models for Stationary Time Series

  • introduction to stationary time series
  • autoregressive models
  • PACF and IACF technical details (self-study)
  • moving average models
  • estimation with unobserved moving average components (self-study)
  • mixed autoregressive moving average models
  • identifying an appropriate autoregressive moving average model
  • estimation and forecasting methods
  • alternatives to Box-Jenkins models

Forecast Models for Nonstationary Time Series

  • statistical tests for trend and seasonality
  • trend models
  • seasonal models
  • alternatives to Box-Jenkins models
  • forecasting the airline passengers time series

Forecast Models with Explanatory Variables

  • ordinary regression models
  • event models
  • time series regression models

Data Preparation for Forecasting

  • working with dates
  • processing time-stamped data
  • reading and modifying time series data
  • working with unique or special dates or frequencies

Software Addressed

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

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