TY  - BOOK
AU  - Keating, Barry; Wilson, J. Holton; 
TI  -  Forecasting and predictive analytics: with ForecastX
SN  - 9789390219452
U1  - 658.401 
PY  - 2021///
CY  - Chennai
PB  -  McGraw-Hill/Irwin 
N1  - hapter 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
N2  - 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.


KEY FEATURES


• Four new chapters on Predictive Analytics, Classification Models, Ensemble Models and Clustering, and Text Mining
• New topics such as trend, seasonal and cyclical components of a time series including new data, steps to obtain better forecasts, mean absolute percent error (MAPE), the ARIMA philosophy of modelling, etc.
• Presents a broad-based survey of business forecasting methods, including subjective and objective approaches
• Provides major updates to predictive analytics, chapter learning objectives, and ForecastX™ software
• Delivers practical forecasting techniques and numerous real-world data sets
ER  -