"Applied Data Mining for Forecasting," 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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