| Judul | Forecasting and Predictive Analytics : With ForecastX (TM) / Barry Keating, J. Holton Wilson |
| Pengarang | Keating, Barry Wilson, J. Holton |
| EDISI | 7th ed |
| Penerbitan | Boston : McGraw-Hill education, 2019 |
| Deskripsi Fisik | xix, 567 p. :ill. ;24 cm.3 |
| ISBN | 978-1-260-08523-5 |
| Subjek | BUSINESS FORECASTING |
| Catatan | 557-567 Contents 1. Introduction to business forecasting and predictive analytics 2. The forecast process, data considerations, and model selection 3. Extrapolation 1, moving avarages and exponential smoothing 4. Extrapolation 2, introduction to forecasting with regression trend models 5. Explanatory models 1. Forecasting with multiple regression causal models 6. Explanatory models 2. Time-series decomposition 7. Explanatory models 3. ARIMA (Box-Jenkins) forecasting models 8. Predictive analytics: Helping to make sense of big data 9. Classifification models: The most used models in analytics 10. Ensemble models and clustering 11. Text mining 12. Forecast/Analytics implementation |
| Bahasa | Inggris |
| Bentuk Karya | Bukan fiksi atau tidak didefinisikan |
| Target Pembaca | Umum |
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| 300 | # | # | $a xix, 567 p. : $b ill. ; $c 24 cm.3 |
| 504 | # | # | $a 557-567 |
| 505 | # | # | $a Contents 1. Introduction to business forecasting and predictive analytics 2. The forecast process, data considerations, and model selection 3. Extrapolation 1, moving avarages and exponential smoothing 4. Extrapolation 2, introduction to forecasting with regression trend models 5. Explanatory models 1. Forecasting with multiple regression causal models 6. Explanatory models 2. Time-series decomposition 7. Explanatory models 3. ARIMA (Box-Jenkins) forecasting models 8. Predictive analytics: Helping to make sense of big data 9. Classifification models: The most used models in analytics 10. Ensemble models and clustering 11. Text mining 12. Forecast/Analytics implementation |
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| 700 | 0 | # | $a Wilson, J. Holton |
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