Applied Data Mining for Forecasting Using SAS. Tim Rey

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Applied Data Mining for Forecasting Using SAS - Tim Rey

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      The correct bibliographic citation for this manual is as follows: Rey, Tim, Arthur Kordon, and Chip Wells. 2012. Applied Data Mining for Forecasting Using SAS®. Cary, NC: SAS Institute Inc.

      Applied Data Mining for Forecasting Using SAS®

      Copyright © 2012, SAS Institute Inc., Cary, NC, USA

      ISBN 978-1-60764-662-4 (Hardcopy)

      ISBN 978-1-62959-799-7 (EPUB)

      ISBN 978-1-62959-800-0 (MOBI)

      ISBN 978-1-61290-093-3 (PDF)

      All rights reserved. Produced in the United States of America.

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      SAS Institute Inc., SAS Campus Drive, Cary, North Carolina 27513-2414.

      July 2012

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      Contents

       Preface

       Chapter 1 Why Industry Needs Data Mining for Forecasting

       1.1 Overview

       1.2 Forecasting Capabilities as a Competitive Advantage

       1.3 The Explosion of Available Time Series Data

       1.4 Some Background on Forecasting

       1.5 The Limitations of Classical Univariate Forecasting

       1.6 What is a Time Series Database?

       1.7 What is Data Mining for Forecasting?

       1.8 Advantages of Integrating Data Mining and Forecasting

       1.9 Remaining Chapters

       Chapter 2 Data Mining for Forecasting Work Process

       2.1 Introduction

       2.2 Work Process Description

       2.2.1 Generic Flowchart

       2.2.2 Key Steps

       2.3 Work Process with SAS Tools

       2.3.1 Data Preparation Steps with SAS Tools

       2.3.2 Variable Reduction and Selection Steps with SAS Tools

       2.3.3 Forecasting Steps with SAS Tools

       2.3.4 Model Deployment Steps with SAS Tools

       2.3.5 Model Maintenance Steps with SAS Tools

       2.3.6 Guidance for SAS Tool Selection Related to Data Mining in Forecasting

       2.4 Work Process Integration in Six Sigma

       2.4.1 Six Sigma in Industry

       2.4.2 The DMAIC Process

       2.4.3 Integration with the DMAIC Process

       Appendix: Project Charter

       Chapter 3 Data

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