list of such methods includes but is not limited to the following: similarity analysis, sequential pattern matching, Principal Component Analysis (PCA), decision trees, co-integration analysis, variable cluster analysis, stepwise regression, and genetic programming.
Unfortunately, the available literature for integrating data mining methods in forecasting is very limited. The existing books on the market are either focused on forecasting methods or on data mining approaches. In addition, there are very few references that discuss the numerous practical issues of applying forecasting in a business setting. The practitioner needs a book that addresses the issues of applied industrial forecasting, gives a framework for integrating data mining and time series forecasting, and gives a methodology for large-scale multivariate industrial forecasting.
Applied Data Mining for Forecasting Using SAS is one of the first books on the market that fills this need.
Purpose of the Book
The purpose of the book is to give the reader an industrial perspective concerning applying data mining for forecasting different business activities using some of the most popular software—SAS Institute's range of SAS products including Base SAS, SAS Enterprise Guide, SAS Enterprise Miner, and SAS Forecast Server. The key topics of the book are as follows:
1 What a practitioner needs to know to successfully apply data mining for forecasting – The first main topic of the book focuses on the ambitious task of giving guidelines to practitioners about building the necessary framework for effective forecasting in a business setting. It covers the issues of justifying the need for industrial forecasting, offering a work process within the popular Six Sigma platform, and discussing the necessary infrastructure and application issues.
2 How data mining improves forecasting – The second key topic of the book clarifies the important question of using data mining for forecasting. Its main focus is on presenting the key data mining methods for variable reduction and selection and their implementation in SAS.
3 How to apply data mining for forecasting in practice – The third key topic of the book covers the central point of interest: the application strategy for business forecasting. It includes a short survey of the key contemporary forecasting methods based on time series and illustrates them with appropriate examples from business practices.
Who This Book Is For
The targeted audience is much broader than the existing scientific communities in forecasting and data mining. The readers who can benefit from this book are described below:
Industrial practitioners – This group includes forecasters in a number of different traditional company departments, such as strategy, sales, marketing, finance, supply-chain, purchasing, and so on. They will benefit from the book by understanding the impact of data mining on forecasting and using the discussed forecasting methods and application methodology to broaden and improve their forecast's performance.
Data miners and modelers – This group consists of the large professional community of users of data mining technologies in different industries. This book will introduce them to contemporary forecasting methods and will demonstrate how they can leverage their data mining skills in the area of industrial forecasting.
Econometricians – This group includes the key community driving the demand for development and application of time series statistical methods, which is at the basis of industrial forecasting. The book will give them substantial information about data mining methods related to time series forecasting, as well as important feedback from industry about the demand for corresponding methods for effective forecasting.
Six Sigma users – Six Sigma is a work process for developing high-quality processes and solutions in industry. It has been accepted as a standard by the majority of global corporations. The estimated users of Six Sigma are tens of thousands of project leaders, called black belts, and hundreds of thousands of technical experts, called green belts. Usually, they use classical statistics in their projects. Data mining for forecasting is a natural extension to Six Sigma for solving complex problems, which both the black and green belts can take advantage of.
Academics – This group includes a large class of academics in both fields (data mining and forecasting) who are not familiar with the research and technical details of the other. They will benefit from the book by using it to broaden their area of expertise and understanding specific requirements for successful practical applications as defined by industrial experts.
Students – Undergraduate and graduate students in technical, economical, and even social disciplines can benefit from the book by understanding the advantages of using data mining in forecasting and its potential for implementation in their specific field. In addition, the book will help students gain knowledge about the practical aspects of forecasting and data mining and the issues faced in real-world applications.
How This Book Is Structured
The first four chapters of the book focus on the main topic of applying data mining for industrial forecasting. Chapter 1 clarifies the business forces that drive the use of data mining for forecasting while Chapter 2 presents a work process, akin to Six Sigma methodologies, that helps to integrate the proposed approach into corporate culture. Chapter 3 describes the critical efforts of building hardware, software, and organizational infrastructures that are needed for the successful application of business forecasting. Chapter 4 gives a systematic view of the key technical and nontechnical application issues as well as a complete checklist for applying data mining for forecasting. The next three chapters focus on presenting the necessary process and methods of data mining as it relates to forecasting. The focus of Chapter 5 is on data collection while Chapter 6 identifies the main data preprocessing steps and emphasizes their critical role for high-quality forecasting. Chapter 7 defines, from a practical perspective, the key data mining methods of forecasting, such as similarity analysis, varcluster analysis, principal component analysis, stepwise regression, decision trees, co-integration analysis, and genetic programming.
Chapters 8 through 11 cover the most important topic of the book—how to define an implementation strategy for successful real-world applications of data mining for forecasting. These chapters present a practitioner's guide of time series forecasting methods that details univariate, multivariate, hierarchical, and nonlinear models. Finally, Chapter 12 illustrates the key topics in applying data mining for forecasting on a real business example.
What This Book Is NOT About
Detailed theoretical description of data mining and forecasting approaches – This book does not include a deep academic presentation of the various data mining and forecasting methods. The reader who is interested in more detailed knowledge on any individual approach is referred to the appropriate resources, such as books, critical papers, and Websites. The focus of the book is on the application of related data mining and forecasting methods. All methods are described and analyzed at the level of detail that will help their broad practical implementation.
Introduction of new data mining and forecasting methods – The book does
