Applied Data Mining for Forecasting Using SASApplied 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. |
Contents
7 | |
Data Mining for Forecasting Infrastructure | 29 |
Issues with Data Mining for Forecasting Application | 39 |
Data Collection | 53 |
Data Preparation | 67 |
A Practitioners Guide of DMM Methods for Forecasting | 95 |
Model Building ARMA Models | 135 |
Model Building ARIMAX or Dynamic Regression Modes | 179 |
Model Building Further Modeling Topics | 205 |
Model Building Alternative Modeling Approaches | 251 |
An Example of Data Mining for Forecasting | 273 |
Appendix A | 309 |
Appendix B | 311 |
313 | |
317 | |
Other editions - View all
Applied Data Mining for Forecasting Using SAS Tim Rey,Arthur Kordon,Chip Wells No preview available - 2012 |
Applied Data Mining for Forecasting Using SAS Tim Rey,Arthur Kordon,Chip Wells No preview available - 2012 |