Documents the syntax of the SAS High-Performance Forecasting product and demonstrates its use through examples.

This guide documents the syntax of the High-Performance Forecasting product and demonstrates its use through examples.

This guide documents the syntax of the High-Performance Forecasting product and demonstrates its use through examples.

In virtually all businesses, poor forecasts lead to ill-devised plans, which can result in the loss of business, customers, and market share, as well as an increase in costs. This title guides you through the SAS High-Performance Forecasting product, designed to help your business succeed by translating large amounts of transactional data into more accurate, automatic forecasts on a large scale. High-Performance Forecasting enables you to accumulate the transactional data into time series data, select the appropriate models and generates forecasts of the time series automatically, and perform trend and seasonal analysis on transactional data for millions of variables. You will learn to forecast intermittent time series (slow moving goods) and to address forecast aggregation hierarchies (by-groups). This guide documents the syntax of the High-Performance Forecasting product and demonstrates its use through examples. This title is also available online. This title is designed to help IT professionals and forecasting experts utilize the full analytical sophistication of SAS High-Performance Forecasting for large-scale forecasting projects. Supports releases 9.1 and higher of SAS software.

This guide documents the syntax of the High-Performance Forecasting product and demonstrates its use through examples. This title is also available online.

This guide documents the syntax of the High-Performance Forecasting product and demonstrates its use through examples. This title is also available online.

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable. This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs.

Praise for Demand-Driven Forecasting A Structured Approach to Forecasting "There are authors of advanced forecasting books who take an academic approach to explaining forecast modeling that focuses on the construction of arcane algorithms and mathematical proof that are not very useful for forecasting practitioners. Then, there are other authors who take a general approach to explaining demand planning, but gloss over technical content required of modern forecasters. Neither of these approaches is well-suited for helping business forecasters critically identify the best demand data sources, effectively apply appropriate statistical forecasting methods, and properly design efficient demand planning processes. In Demand-Driven Forecasting, Chase fills this void in the literature and provides the reader with concise explanations for advanced statistical methods and credible business advice for improving ways to predict demand for products and services. Whether you are an experienced professional forecasting manager, or a novice forecast analyst, you will find this book a valuable resource for your professional development." —Daniel Kiely, Senior Manager, Epidemiology, Forecasting & Analytics, Celgene Corporation "Charlie Chase has given forecasters a clear, responsible approach for ending the timeless tug of war between the need for 'forecast rigor' and the call for greater inclusion of 'client judgment.' By advancing the use of 'domain knowledge' and hypothesis testing to enrich base-case forecasts, he has empowered professional forecasters to step up and impact their companies' business results favorably and profoundly, all the while enhancing the organizational stature of forecasters broadly." —Bob Woodard, Vice President, Global Consumer and Customer Insights, Campbell Soup Company

Applied Data Mining for Forecasting Using SAS, by Tim Rey, Arthur Kordon, and Chip Wells, introduces and describes approaches for mining large time series data sets. Written for forecasting practitioners, engineers, statisticians, and economists, the book details how to select useful candidate input variables for time series regression models in environments when the number of candidates is large, and identifies the correlation structure between selected candidate inputs and the forecast variable. This book is essential for forecasting practitioners who need to understand the practical issues involved in applied forecasting in a business setting. Through numerous real-world examples, the authors demonstrate how to effectively use SAS software to meet their industrial forecasting needs. This book is part of the SAS Press program.