Applied data mining for forecasting using SAS /

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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Rey, Tim
Autor Corporativo: SAS Institute
Otros Autores: Kordon, Arthur K., Wells, Chip
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cary, N.C. : SAS Institute, 2012.
Temas:
SAS (Computer file)
Data mining.
Forecasting.
Exploration de données (Informatique)
Prévision.
COMPUTERS > Database Management > Data Mining.
Data mining
Forecasting
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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245 1 0 |a Applied data mining for forecasting using SAS /  |c Tim Rey, Arthur Kordon, Chip Wells. 
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505 0 |a 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. 
588 0 |a Print version record. 
520 8 |a 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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