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Applied Data Mining for Forecasting Using SAS
Applied Data Mining for Forecasting Using SAS
Knygos.lt klubas Knygos.lt nariams
105,55 €
-30%
Įprastai
150,79 €
  • Išsiųsime per 12–18 d.d.
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 variab…
  • Leidėjas:
  • ISBN-10: 1607646625
  • ISBN-13: 9781607646624
  • Formatas: 21 x 27.9 x 1.8 cm, minkšti viršeliai
  • Kalba: Anglų

Applied Data Mining for Forecasting Using SAS (el. knyga) (skaityta knyga) | knygos.lt

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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.

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  • Autorius: Tim Rey
  • Leidėjas:
  • ISBN-10: 1607646625
  • ISBN-13: 9781607646624
  • Formatas: 21 x 27.9 x 1.8 cm, minkšti viršeliai
  • Kalba: Anglų

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.

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