Applied Data Mining for Forecasting Using SAS. Tim Rey
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3.3.1 Data Collection Software
3.3.2 Data Preparation Software
3.3.5 Software Selection Criteria
3.4.1 Internal Data Infrastructure
3.4.2 External Data Infrastructure
3.5 Organizational Infrastructure
3.5.1 Developers Infrastructure
3.5.3 Work Process Implementation
Chapter 4 Issues with Data Mining for Forecasting Application
4.2 Technical Issues
4.2.1 Data Quality Issues
4.2.2 Data Mining Methods Limitations
4.2.3 Forecasting Methods Limitations
4.3 Nontechnical Issues
4.3.1 Managing Forecasting Expectations
4.3.2 Handling Politics of Forecasting
4.3.3 Avoiding Bad Practices
4.3.4 Forecasting Aphorisms
4.4 Checklist “Are We Ready?”
5.1 Introduction
5.2 System Structure and Data Identification
5.2.1 Mind-Mapping
5.2.2 System Structure Knowledge Acquisition
5.2.3 Data Structure Identification
5.3 Data Definition
5.3.1 Data Sources
5.3.2 Metadata
5.4 Data Extraction
5.4.1 Internal Data Extraction
5.4.2 External Data Extraction
5.5 Data Alignment
5.5.1 Data Alignment to a Business Structure
5.5.2 Data Alignment to Time
5.6 Data Collection Automation for Model Deployment
5.6.1 Differences between Data Collection for Model Development and Deployment
5.6.2 Data Collection Automation for Model Deployment
6.1 Overview
6.2 Transactional Data Versus Time Series Data
6.3 Matching Frequencies
6.3.1 Contracting
6.3.2 Expanding
6.4 Merging
6.5 Imputation
6.6 Outliers
6.7 Transformations
6.8 Summary
Chapter 7 A Practitioner's Guide of DMM Methods for Forecasting
7.1 Overview
7.2 Methods for Variable Reduction
Traditional Data Mining
Time Series Approach
7.3 Methods for Variable Selection
Traditional Data Mining
Example for Variable Selection
Variable Selection Based on Pearson Product-Moment Correlation Coefficient
Variable Selection Based on Stepwise Regression
Variable Selection Based on the SAS Enterprise Miner Variable Selection Node
Variable Selection Based on the SAS Enterprise Miner Partial Least Squares Node
Variable Selection Based on Decision Trees
Variable Selection Based on Genetic Programming
Comparison of Data Mining Variable Selection Results
7.4 Time Series Approach
7.5 Summary
Chapter 8 Model Building: ARMA Models
Introduction
8.1 ARMA Models
8.1.1 AR Models: Concepts and Application
8.1.2 Moving Average Models: Concepts and Application
8.1.3 Auto Regressive Moving Average (ARMA) Models
Appendix 1: Useful Technical Details
Appendix 2: The “I” in ARIMA
Chapter 9 Model Building: ARIMAX or Dynamic Regression Modes
Introduction
9.1 ARIMAX Concepts
9.2 ARIMAX Applications
Appendix: Prewhitening and Other Topics Associated with Interval-Valued Input Variables
Chapter 10 Model Building: Further Modeling Topics
Introduction
10.1 Creating Time Series Data and Data Hierarchies Using Accumulation and Aggregation Methods
Introduction