Forecasting and predictive analytics: with ForecastX
- 7th
- Chennai McGraw Hill Education (India) Pvt. Ltd. 2022
- xviii, 539 p.
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
OVERVIEW
The seventh edition of Forecasting and Predictive Analytics with ForecastX⢠builds on the success of the previous editions. While a number of significant changes have been made in this edition, it remains a book about prediction methods for managers, forecasting practitioners, data scientists, and students aspiring to become business professionals and have a need to understand practical issues related to prediction in all its forms. The text is designed to lead through the most helpful techniques in any prediction effort. Most of the examples in the book are based on actual historical data and the techniques are explained as procedures that users may replicate with their own data.