
Applied Data Mining for Forecasting Using SAS
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Content
- Intro
- Contents
- Preface
- Chapter 1: Why Industry Needs Data Mining For Forecasting
- 1.1 Overview
- 1.2 Forecasting Capabilities as a Competitive Advantage
- 1.3 The Explosion of Available Time Series Data
- 1.4 Some Background on Forecasting
- 1.5 The Limitations of Classical Univariate Forecasting
- 1.6 What is a Time Series Database?
- 1.7 What is Data Mining for Forecasting?
- 1.8 Advantages of Integrating Data Mining and Forecasting
- 1.9 Remaining Chapters
- Chapter 2: Data Mining for Forecasting Work Process
- 2.1 Introduction
- 2.2 Work Process Description
- 2.2.1 Generic Flowchart
- 2.2.2 Key Steps
- 2.3 Work Process with SAS Tools
- 2.3.1 Data Preparation Steps with SAS Tools
- 2.3.2 Variable Reduction and Selection Steps with SAS Tools
- 2.3.3 Forecasting Steps with SAS Tools
- 2.3.4 Model Deployment Steps with SAS Tools
- 2.3.5 Model Maintenance Steps with SAS Tools
- 2.3.6 Guidance for SAS Tool Selection Related to Data Mining in Forecasting
- 2.4 Work Process Integration in Six Sigma
- 2.4.1 Six Sigma in Industry
- 2.4.2 The DMAIC Process
- 2.4.3 Integration with the DMAIC Process
- Appendix: Project Charter
- Chapter 3: Data Mining for Forecasting Infrastructure
- 3.1 Introduction
- 3.2 Hardware Infrastructure
- 3.2.1 Personal Computers Network Infrastructure
- 3.2.2 Client/Server Infrastructure
- 3.2.3 Cloud Computing Infrastructure
- 3.3 Software Infrastructure
- 3.3.1 Data Collection Software
- 3.3.2 Data Preparation Software
- 3.3.3 Data Mining Software
- 3.3.4 Forecasting Software
- 3.3.5 Software Selection Criteria
- 3.4 Data Infrastructure
- 3.4.1 Internal Data Infrastructure
- 3.4.2 External Data Infrastructure
- 3.5 Organizational Infrastructure
- 3.5.1 Developers Infrastructure
- 3.5.2 Users Infrastructure
- 3.5.3 Work Process Implementation
- 3.5.4 Integration with IT
- Chapter 4: Issues with Data Mining for Forecasting Application
- 4.1 Introduction
- 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?"
- Chapter 5: Data Collection
- 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
- Chapter 6: Data Preparation
- 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
- 7.3 Methods for Variable Selection
- 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
- Chapter 9: Model Building: ARIMAX or Dynamic Regression Modes
- Introduction
- 9.1 ARIMAX Concepts
- 9.2 ARIMAX Applications
- Chapter 10: Model Building: Further Modeling Topics
- Introduction
- 10.1 Creating Time Series Data and Data Hierarchies Using Accumulation and Aggregation Methods
- Introduction
- Creating Time Series Data Using Accumulation Methods
- Creating Data Hierarchies Using Aggregation Methods
- 10.2 Statistical Forecast Reconciliation
- 10.3 Intermittent Demand
- 10.4 High-Frequency Data and Mixed-Frequency Forecasting
- Introduction
- High-Frequency Data
- Mixed-Interval Forecasting
- 10.5 Holdout Samples and Forecast Model Selection in Time Series
- Introduction
- 10.6 Planning Versus Forecasting and Manual Overrides
- 10.7 Scenario-Based Forecasting
- 10.8 New Product Forecasting
- Chapter 11: Model Building: Alternative Modeling Approaches
- 11.1 Nonlinear Forecasting Models
- 11.1.1 Nonlinear Modeling Features
- 11.1.2 Forecasting Models Based on Neural Networks
- 11.1.3 Forecasting Models Based on Support Vector Machines
- 11.1.4 Forecasting Models Based on Evolutionary Computation
- 11.2 More Modeling Alternatives
- 11.2.1 Multivariate Models
- 11.2.2 Unobserved Component Models (UCM)
- Chapter 12: An Example of Data Mining for Forecasting
- 12.1 The Business Problem
- 12.2 The Charter
- 12.3 The Mind Map
- 12.4 Data Sources
- 12.5 Data Prep
- 12.6 Exploratory Analysis and Data Preprocessing
- 12.7 X Variable Imputation
- 12.8 Variable Reduction and Selection
- 12.9 Modeling
- 12.10 Summary
- Appendix A
- Appendix B
- References
- Index
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