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Applied data mining for forecasting using SAS

Author: Tim Rey; Arthur K Kordon; Chip Wells; SAS Institute.
Publisher: Cary, N.C. : SAS Institute, 2012.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Rey, Tim.
Applied data mining for forecasting using SAS.
Cary, N.C. : SAS Institute, 2012
(OCoLC)805731870
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Tim Rey; Arthur K Kordon; Chip Wells; SAS Institute.
ISBN: 9781612900933 1612900933 1607646625 9781607646624
OCLC Number: 811564898
Description: 1 online resource (x, 324 pages) : illustrations
Contents: Why industry needs data mining for forecasting --
Data mining for forecasting work process --
Data mining for forecasting infrastructure --
Issues with data mining for forecasting application --
Data collection --
Data preparation --
A practitioner's guide to DMM methods for forecasting --
Model building: ARMA models --
Model building: ARIMAX or dynamic regression modes --
Model building: further modeling topics --
Model building: alternative modeling approaches --
An example of data mining for forecasting.
Responsibility: Tim Rey, Arthur Kordon, Chip Wells.

Abstract:

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.

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