Intro
Preface
The Target Audience
The Book's Competitive Comparison
The Book's Structure
Acknowledgments
Contents
List of Figures
List of Tables
Part I The Analytics Quest: The Drive for Rich Information
1 Decisions, Information, and Data
1.1 Decisions and Uncertainty
1.1.1 What Is Uncertainty?
1.1.2 The Cost of Uncertainty
1.1.3 Reducing Uncertainty
1.1.4 The Scale-View of Decision Makers
1.1.5 Rich Information Requirements
1.2 A Data and Information Framework
1.3 Rich Information Predictive Extraction Methods

1.3.1 Informal Analytical Components
1.3.2 Formal Analytical Components
1.4 A Systems Perspective
1.5 This Book's Focus
2 A Systems Perspective
2.1 Introduction to Complex Systems
2.2 Types of Systems: Examples
2.2.1 Economic Complex Systems
2.2.2 Business Complex Systems
2.2.3 Other Types of Complex Systems
2.3 Predictions, Forecasts, and Business Complex Systems
2.4 System Complexity and Scale-View
2.5 Simulations and Scale-View
Part II Predictive Analytics: Background
3 Information Extraction: Basic Time Series Methods

3.1 Overview of Extraction Methods
3.2 Predictions as Time Series
3.3 Time Series and Forecasting Notation
3.4 The Backshift Operator: An Overview
3.5 Naive Forecasting Models
3.6 Constant Mean Model
3.6.1 Properties of a Variance
3.6.2 h-Step Ahead Forecasts
3.7 Random Walk Model
3.7.1 Basic Random Walk Model
3.7.2 Random Walk with Drift
3.8 Simple Moving Averages Model
3.8.1 Weighted Moving Average Model
3.8.2 Exponential Averaging
3.9 Linear Trend Models
3.9.1 Linear Trend Model Estimation
3.9.2 Linear Trend Extension

3.9.3 Linear Trend Prediction
3.10 Appendix
3.10.1 Reproductive Property of Normals
3.10.2 Proof of MSE = V() + Bias2
3.10.3 Backshift Operator Result
3.10.4 Variance of h-Step Ahead Random Walk Forecast
3.10.5 Exponential Moving Average Weights
3.10.6 Flat Exponential Averaging Forecast
3.10.7 Variance of a Random Variable
3.10.8 Background on the Exponential Growth Model
4 Information Extraction: Advanced Time Series Methods
4.1 The Breadth of Time Series Data
4.2 Introduction to Linear Predictive Models
4.2.1 Feature Specification

4.3 Data Preprocessing
4.4 Model Fit vs. Predictability
4.5 Case Study: Predicting Total Vehicle Sales
4.5.1 Modeling Data: Overview
4.5.2 Modeling Data: Some Analysis
4.5.3 Linear Model for New Car Sales
4.6 Stochastic (Box-Jenkins) Time Series Models
4.6.1 Model Identification
4.6.2 Brief Introduction to Stationarity
4.6.3 Correcting for Non-stationarity
4.6.4 Predicting with the AR(1) Model
4.7 Advanced Time Series Models
4.8 Autoregressive Distributed Lag Models
4.8.1 Short-Run and Long-Run Effects
4.9 Appendix
4.9.1 Chow Test Functions