ISE Forecasting and Predictive Analytics with Forecast X (TM)
Autor Barry Keating, J. Holton Wilson, John Solutions Inc.en Limba Engleză Paperback – 4 mar 2018
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Specificații
ISBN-13: 9781260085235
ISBN-10: 1260085236
Pagini: 592
Dimensiuni: 232 x 207 x 23 mm
Greutate: 0.85 kg
Ediția:7
Editura: McGraw Hill Education
Colecția McGraw-Hill
Locul publicării:United States
ISBN-10: 1260085236
Pagini: 592
Dimensiuni: 232 x 207 x 23 mm
Greutate: 0.85 kg
Ediția:7
Editura: McGraw Hill Education
Colecția McGraw-Hill
Locul publicării:United States
Cuprins
Chapter 1: Introduction to Business Forecasting and Predictive Analytics
Chapter 2:The Forecast Process, Data Considerations, and Model Selection
Chapter 3:Extrapolation 1. Moving Averages and Exponential Smoothing
Chapter 4:Extrapolation 2. Introduction to Forecasting with Regression Trend Models
Chapter 5:Explanatory Models 1. Forecasting with Multiple Regression Causal Models
Chapter 6:Explanatory Models 2. Time-Series Decomposition
Chapter 7:Explanatory Models 3. ARIMA (Box-Jenkins) Forecasting Models
Chapter 8:Predictive Analytics: Helping to Make Sense of Big Data
Chapter 9:Classification Models: The Most Used Models in Analytics
Chapter 10:Ensemble Models and Clustering
Chapter 11:Text Mining
Chapter 12:Forecast/Analytics Implementation
Chapter 2:The Forecast Process, Data Considerations, and Model Selection
Chapter 3:Extrapolation 1. Moving Averages and Exponential Smoothing
Chapter 4:Extrapolation 2. Introduction to Forecasting with Regression Trend Models
Chapter 5:Explanatory Models 1. Forecasting with Multiple Regression Causal Models
Chapter 6:Explanatory Models 2. Time-Series Decomposition
Chapter 7:Explanatory Models 3. ARIMA (Box-Jenkins) Forecasting Models
Chapter 8:Predictive Analytics: Helping to Make Sense of Big Data
Chapter 9:Classification Models: The Most Used Models in Analytics
Chapter 10:Ensemble Models and Clustering
Chapter 11:Text Mining
Chapter 12:Forecast/Analytics Implementation