| Description |
1 online resource (x, 324 pages) : illustrations |
| Bibliography |
Includes bibliographical references (pages 313-316) and index. |
| 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. |
| 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 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. |
| Subject |
SAS (Computer file)
|
|
SAS (Computer file) |
|
SAS (Computer file) |
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Data mining.
|
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Forecasting.
|
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Exploration de données (Informatique) |
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Prévision. |
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Data mining |
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Forecasting |
| Added Author |
Kordon, Arthur K.
|
|
Wells, Chip.
|
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SAS Institute.
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| Other Form: |
Print version: Rey, Tim. Applied data mining for forecasting using SAS. Cary, N.C. : SAS Institute, 2012 9781607646624 (OCoLC)805731870 |
| ISBN |
9781612900933 (electronic bk.) |
|
1612900933 (electronic bk.) |
|
1607646625 |
|
9781607646624 |
| Standard No. |
9781607646624 |
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