ISE Forecasting and Predictive Analytics with Forecast X (TM)
7th Edition
Published on 4. March 2018
Book
Paperback/Softback
594 pages
978-1-260-08523-5 (ISBN)
Description
The Seventh Edition of Business Forecasting is the most practical forecasting book on the market with the most powerful software-Forecast X.
This edition presents a broad-based survey of business forecasting methods including subjective and objective approaches. As always, the author team of Keating and Wilson deliver practical how-to forecasting techniques, along with dozens of real world data sets while theory and math are held to a minimum.
This edition presents a broad-based survey of business forecasting methods including subjective and objective approaches. As always, the author team of Keating and Wilson deliver practical how-to forecasting techniques, along with dozens of real world data sets while theory and math are held to a minimum.
More details
Edition
7th edition
Language
English
Place of publication
OH
United States
Target group
College/higher education
US School Grade: From College Freshman to College Graduate Student
Dimensions
Height: 231 mm
Width: 185 mm
Thickness: 23 mm
Weight
717 gr
ISBN-13
978-1-260-08523-5 (9781260085235)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Software company producing Forecast X, which will accompany this edition of BUSINESS FORECASTING.
Content
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