Applied Data Mining for Forecasting Using SAS

Applied Data Mining for Forecasting Using SAS
Author: Tim Rey
Publisher: SAS Institute
Total Pages: 336
Release: 2012-07-02
Genre: Computers
ISBN: 1612900933


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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.


Applied Data Mining for Forecasting Using SAS
Language: en
Pages: 336
Authors: Tim Rey
Categories: Computers
Type: BOOK - Published: 2012-07-02 - Publisher: SAS Institute

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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
Applied Data Mining for Forecasting Using SAS
Language: en
Pages: 336
Authors: Tim Rey
Categories: Computers
Type: BOOK - Published: 2012-07 - Publisher: SAS Institute

GET EBOOK

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
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Type: BOOK - Published: 2018-03-14 - Publisher: SAS Institute

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To use statistical methods and SAS applications to forecast the future values of data taken over time, you need only follow this thoroughly updated classic on t
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Data mining can be defined as the process of selection, explorationand modelling of large databases, in order to discover models andpatterns. The increasing ava
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