This book aims to provide a practical guide by a set of real world applications of grey systems to social networks, energy management, transportation, natural disaster management, etc. As an emerging subject on data analysis and uncertainty modelling, the theory of grey systems and its applications have seen significant progress in recent years.  However, researchers are still challenged with difficulties in differentiating grey systems from other similar models and a concise and practical guide for their real world applications. This is especially true for researchers in Europe and North American. This book may provide the answer for that. This book is a result of work within the Leverhulme Trust International Research Network on Grey Systems and its Applications. Readers may regard the book as valuable reference in the related fields.

This book aims to provide a practical guide by a set of real world applications of grey systems to social networks, energy management, transportation, natural disaster management, etc. As an emerging subject on data analysis and uncertainty modelling, the theory of grey systems and its applications have seen significant progress in recent years.  However, researchers are still challenged with difficulties in differentiating grey systems from other similar models and a concise and practical guide for their real world applications. This is especially true for researchers in Europe and North American. This book may provide the answer for that. This book is a result of work within the Leverhulme Trust International Research Network on Grey Systems and its Applications. Readers may regard the book as valuable reference in the related fields.

The book covers up-to-date theoretical and applied advances in grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the authors, including simplified form of grey number, general grey number and the operations of grey numbers; the axiomatic system of buffer operators and a series of weakening and strengthening operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative and three dimension degree, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute intelligent grey target decision models, weight vector group withkernel and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc. This book will be appropriate as a reference and/or professional book for courses of grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, medicine, astronomy, earth science, economics, and management. It can also be utilized by researchers and practitioners in research institutions, business entities, and government agencies.

This book focuses on the main advancements made in the economics and social sciences field through the use of grey systems theory. As a result, it addresses both the state of the art and the applications of grey systems theory in economics and social sciences. The book is structured in eight main chapters, covering the following topics: the state of the art in the grey systems theory research in economics and social sciences, which includes a bibliometric analysis, a selection of the most well-cited papers in the field, and a selection of applications in which the grey systems theory is used in the areas of suppliers selection,  risk assessment, public opinion assessment, linear programming, complex projects management, social media analysis, and natural language processing Each chapter gives an overview of a particular economic or social sciences topic, providing an explanation on the main terms and methods used for solving the problem, including the notations, terminology, and the needed steps to solve it. A practical application is presented in most of the chapters, while in the others, a series of case studies are presented from the literature and discussed in depth in terms of methods used and advantages brought by each of these methods. The last chapter discusses the hybridization cases in which the grey systems theory has been or can be successfully used along with other artificial intelligence methods and techniques for a more advanced analysis in the economics and social sciences field. The reasoning and the explanations used in the book are easy to understand for the interested persons who are not familiar to the field and want to learn more related on how the grey systems theory can be applied to economics and social sciences. As for the experts in this field, this book can be a good referral point for developing new areas of research by combining the advantages of the grey systems theory with other theories within the field.

This book focuses on the main advancements made in the economics and social sciences field through the use of grey systems theory. As a result, it addresses both the state of the art and the applications of grey systems theory in economics and social sciences. The book is structured in eight main chapters, covering the following topics: the state of the art in the grey systems theory research in economics and social sciences, which includes a bibliometric analysis, a selection of the most well-cited papers in the field, and a selection of applications in which the grey systems theory is used in the areas of suppliers selection,  risk assessment, public opinion assessment, linear programming, complex projects management, social media analysis, and natural language processing Each chapter gives an overview of a particular economic or social sciences topic, providing an explanation on the main terms and methods used for solving the problem, including the notations, terminology, and the needed steps to solve it. A practical application is presented in most of the chapters, while in the others, a series of case studies are presented from the literature and discussed in depth in terms of methods used and advantages brought by each of these methods. The last chapter discusses the hybridization cases in which the grey systems theory has been or can be successfully used along with other artificial intelligence methods and techniques for a more advanced analysis in the economics and social sciences field. The reasoning and the explanations used in the book are easy to understand for the interested persons who are not familiar to the field and want to learn more related on how the grey systems theory can be applied to economics and social sciences. As for the experts in this field, this book can be a good referral point for developing new areas of research by combining the advantages of the grey systems theory with other theories within the field.

This book covers up-to-date theoretical and applied advances in fractional order grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models, and methods in the book are original by the author, including grey system model with the fractional order accumulation and its properties, the relationship between the sample size and the stability of grey forecasting model, applications of the fractional order grey models in sustainable development and energy consumption forecasting, grey forecasting model for the middle size data, etc.This book is appropriate as a reference and/or professional book for courses of environmental management and grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, environmental science, earth science, economics, and management. It is also utilized by researchers and practitioners in research institutions, business entities, and government agencies.

This book covers up-to-date theoretical and applied advances in fractional order grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models, and methods in the book are original by the author, including grey system model with the fractional order accumulation and its properties, the relationship between the sample size and the stability of grey forecasting model, applications of the fractional order grey models in sustainable development and energy consumption forecasting, grey forecasting model for the middle size data, etc.This book is appropriate as a reference and/or professional book for courses of environmental management and grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, environmental science, earth science, economics, and management. It is also utilized by researchers and practitioners in research institutions, business entities, and government agencies.

This book illustrates the application of grey systems theory to management science research, by developing selection procedures for grey analytical methods suitable for forecasting, decision-making, and analysing variable relationships. Highlights the similarity between grey system theory and other uncertainty modelling methods, introducing demarcation criteria between grey system theory, fuzzy logic, and statistical approaches. The book leverages the concept of a "grey space" to move beyond the intuitive understanding of grey, presenting a logical and mathematical model of the concept. Connects grey information with traditional management research paradigms and strategies, serving as a methodological guide to grey methods tailored for social sciences, especially management research.The aim is to facilitate management science readers' understanding of how grey systems theory can be applied in research, providing specific guidance on the use of grey methods in management studies and discussing their possibilities and limitations. This work provides methodological guidelines for conducting research in management science.The primary audience includes management science researchers and doctoral students dealing with empirical research where incomplete and sparse data are a critical factor, and scholars in the grey systems theory community. The book introduces new concepts from the axiomatisation of greyness, appealing to specialists interested in this area.

This book illustrates the application of grey systems theory to management science research, by developing selection procedures for grey analytical methods suitable for forecasting, decision-making, and analysing variable relationships. Highlights the similarity between grey system theory and other uncertainty modelling methods, introducing demarcation criteria between grey system theory, fuzzy logic, and statistical approaches. The book leverages the concept of a "grey space" to move beyond the intuitive understanding of grey, presenting a logical and mathematical model of the concept. Connects grey information with traditional management research paradigms and strategies, serving as a methodological guide to grey methods tailored for social sciences, especially management research.The aim is to facilitate management science readers' understanding of how grey systems theory can be applied in research, providing specific guidance on the use of grey methods in management studies and discussing their possibilities and limitations. This work provides methodological guidelines for conducting research in management science.The primary audience includes management science researchers and doctoral students dealing with empirical research where incomplete and sparse data are a critical factor, and scholars in the grey systems theory community. The book introduces new concepts from the axiomatisation of greyness, appealing to specialists interested in this area.

This book aims to present an overview of grey system models for time series modelling and forecasting. It is about modelling and forecasting time series with ordinary differential equations, especially when the available samples are extremely limited. Grey system models (GSM) develop sequence operators to nonparametrically identify the underlying dynamics from the limited observations. This book concerns about two important modelling themes, small sample and poor information. The former focuses on the mechanism and methodology of GSMs for small-sample real-number time series, and the latter on the uncertainty quantification of grey number together with its small-sample modelling principles. In this book, a broad entry point to applied data science for students majoring in economic, management science, and engineering is applied, covering a wide range of topics from basic introductory material up to research-level techniques.

This book aims to present an overview of grey system models for time series modelling and forecasting. It is about modelling and forecasting time series with ordinary differential equations, especially when the available samples are extremely limited. Grey system models (GSM) develop sequence operators to nonparametrically identify the underlying dynamics from the limited observations. This book concerns about two important modelling themes, small sample and poor information. The former focuses on the mechanism and methodology of GSMs for small-sample real-number time series, and the latter on the uncertainty quantification of grey number together with its small-sample modelling principles. In this book, a broad entry point to applied data science for students majoring in economic, management science, and engineering is applied, covering a wide range of topics from basic introductory material up to research-level techniques.

This book introduces the grey prediction model methodology and its applications as a professional book. It involves the key concepts, data characteristics of modelling objects and the latest research achievements in grey system. The grey prediction models for homogeneous and non-homogeneous exponential sequences, saturated S-shaped sequences and special sequences have been introduced.  This book combines the classical models and the latest models, case studies and model applications, modelling methods, and MATLAB programs in grey system. It is a guidebook for both new learners and professionals of grey prediction models.

This book introduces the grey prediction model methodology and its applications as a professional book. It involves the key concepts, data characteristics of modelling objects and the latest research achievements in grey system. The grey prediction models for homogeneous and non-homogeneous exponential sequences, saturated S-shaped sequences and special sequences have been introduced.  This book combines the classical models and the latest models, case studies and model applications, modelling methods, and MATLAB programs in grey system. It is a guidebook for both new learners and professionals of grey prediction models.

This open access book is the 2nd edition involving the update on data, methods and models of Grey Systems. It covers up-to-date theoretical and applied advances in grey systems from across the world, and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the author, including kernel, degree of greyness of grey number, simplified form of grey number, general grey number and the operation system; the axiomatic system of buffer operators and a series of weakening and strengthening buffer operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative, three dimension, and grey relational analysis model for cross-sequences, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute weighted intelligent grey target decision models, kernel weight vector group and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc.The revision includes: (1) Added new achievements made in recent years, such as the moving average denoise operator, a series of negative grey relational models, grey relational model for cross-sequences, standard uncertainty numbers and their operations, adaptive Grey Prediction Models and so on; (2) Important data related to the development of grey system theory has been updated; (3) Research reviews have been added to each chapter, and a large number of references have been added; (4)Updated application examples of commonly used models and methods.This book will be appropriate as a reference and/or textbook for courses of grey system theory for graduate students or high level undergraduate students, majoring in various fields of natural sciences, social sciences and engineering technology. It can also be utilized by researchers and technicians in research institutions, business entities, and government agencies.

This book aims to provide a practical guide by a set of real world applications of grey systems to social networks, energy management, transportation, natural disaster management, etc. As an emerging subject on data analysis and uncertainty modelling, the theory of grey systems and its applications have seen significant progress in recent years.  However, researchers are still challenged with difficulties in differentiating grey systems from other similar models and a concise and practical guide for their real world applications. This is especially true for researchers in Europe and North American. This book may provide the answer for that. This book is a result of work within the Leverhulme Trust International Research Network on Grey Systems and its Applications. Readers may regard the book as valuable reference in the related fields.

This book aims to provide a practical guide by a set of real world applications of grey systems to social networks, energy management, transportation, natural disaster management, etc. As an emerging subject on data analysis and uncertainty modelling, the theory of grey systems and its applications have seen significant progress in recent years.  However, researchers are still challenged with difficulties in differentiating grey systems from other similar models and a concise and practical guide for their real world applications. This is especially true for researchers in Europe and North American. This book may provide the answer for that. This book is a result of work within the Leverhulme Trust International Research Network on Grey Systems and its Applications. Readers may regard the book as valuable reference in the related fields.

The book covers up-to-date theoretical and applied advances in grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the authors, including simplified form of grey number, general grey number and the operations of grey numbers; the axiomatic system of buffer operators and a series of weakening and strengthening operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative and three dimension degree, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute intelligent grey target decision models, weight vector group withkernel and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc. This book will be appropriate as a reference and/or professional book for courses of grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, medicine, astronomy, earth science, economics, and management. It can also be utilized by researchers and practitioners in research institutions, business entities, and government agencies.

This book focuses on the main advancements made in the economics and social sciences field through the use of grey systems theory. As a result, it addresses both the state of the art and the applications of grey systems theory in economics and social sciences. The book is structured in eight main chapters, covering the following topics: the state of the art in the grey systems theory research in economics and social sciences, which includes a bibliometric analysis, a selection of the most well-cited papers in the field, and a selection of applications in which the grey systems theory is used in the areas of suppliers selection,  risk assessment, public opinion assessment, linear programming, complex projects management, social media analysis, and natural language processing Each chapter gives an overview of a particular economic or social sciences topic, providing an explanation on the main terms and methods used for solving the problem, including the notations, terminology, and the needed steps to solve it. A practical application is presented in most of the chapters, while in the others, a series of case studies are presented from the literature and discussed in depth in terms of methods used and advantages brought by each of these methods. The last chapter discusses the hybridization cases in which the grey systems theory has been or can be successfully used along with other artificial intelligence methods and techniques for a more advanced analysis in the economics and social sciences field. The reasoning and the explanations used in the book are easy to understand for the interested persons who are not familiar to the field and want to learn more related on how the grey systems theory can be applied to economics and social sciences. As for the experts in this field, this book can be a good referral point for developing new areas of research by combining the advantages of the grey systems theory with other theories within the field.

This book focuses on the main advancements made in the economics and social sciences field through the use of grey systems theory. As a result, it addresses both the state of the art and the applications of grey systems theory in economics and social sciences. The book is structured in eight main chapters, covering the following topics: the state of the art in the grey systems theory research in economics and social sciences, which includes a bibliometric analysis, a selection of the most well-cited papers in the field, and a selection of applications in which the grey systems theory is used in the areas of suppliers selection,  risk assessment, public opinion assessment, linear programming, complex projects management, social media analysis, and natural language processing Each chapter gives an overview of a particular economic or social sciences topic, providing an explanation on the main terms and methods used for solving the problem, including the notations, terminology, and the needed steps to solve it. A practical application is presented in most of the chapters, while in the others, a series of case studies are presented from the literature and discussed in depth in terms of methods used and advantages brought by each of these methods. The last chapter discusses the hybridization cases in which the grey systems theory has been or can be successfully used along with other artificial intelligence methods and techniques for a more advanced analysis in the economics and social sciences field. The reasoning and the explanations used in the book are easy to understand for the interested persons who are not familiar to the field and want to learn more related on how the grey systems theory can be applied to economics and social sciences. As for the experts in this field, this book can be a good referral point for developing new areas of research by combining the advantages of the grey systems theory with other theories within the field.

This book covers up-to-date theoretical and applied advances in fractional order grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models, and methods in the book are original by the author, including grey system model with the fractional order accumulation and its properties, the relationship between the sample size and the stability of grey forecasting model, applications of the fractional order grey models in sustainable development and energy consumption forecasting, grey forecasting model for the middle size data, etc.This book is appropriate as a reference and/or professional book for courses of environmental management and grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, environmental science, earth science, economics, and management. It is also utilized by researchers and practitioners in research institutions, business entities, and government agencies.

This book covers up-to-date theoretical and applied advances in fractional order grey systems theory from across the world and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models, and methods in the book are original by the author, including grey system model with the fractional order accumulation and its properties, the relationship between the sample size and the stability of grey forecasting model, applications of the fractional order grey models in sustainable development and energy consumption forecasting, grey forecasting model for the middle size data, etc.This book is appropriate as a reference and/or professional book for courses of environmental management and grey system theory for graduate students or high-level undergraduate students, majoring in areas of science, technology, agriculture, environmental science, earth science, economics, and management. It is also utilized by researchers and practitioners in research institutions, business entities, and government agencies.

This book illustrates the application of grey systems theory to management science research, by developing selection procedures for grey analytical methods suitable for forecasting, decision-making, and analysing variable relationships. Highlights the similarity between grey system theory and other uncertainty modelling methods, introducing demarcation criteria between grey system theory, fuzzy logic, and statistical approaches. The book leverages the concept of a "grey space" to move beyond the intuitive understanding of grey, presenting a logical and mathematical model of the concept. Connects grey information with traditional management research paradigms and strategies, serving as a methodological guide to grey methods tailored for social sciences, especially management research.The aim is to facilitate management science readers' understanding of how grey systems theory can be applied in research, providing specific guidance on the use of grey methods in management studies and discussing their possibilities and limitations. This work provides methodological guidelines for conducting research in management science.The primary audience includes management science researchers and doctoral students dealing with empirical research where incomplete and sparse data are a critical factor, and scholars in the grey systems theory community. The book introduces new concepts from the axiomatisation of greyness, appealing to specialists interested in this area.

This book illustrates the application of grey systems theory to management science research, by developing selection procedures for grey analytical methods suitable for forecasting, decision-making, and analysing variable relationships. Highlights the similarity between grey system theory and other uncertainty modelling methods, introducing demarcation criteria between grey system theory, fuzzy logic, and statistical approaches. The book leverages the concept of a "grey space" to move beyond the intuitive understanding of grey, presenting a logical and mathematical model of the concept. Connects grey information with traditional management research paradigms and strategies, serving as a methodological guide to grey methods tailored for social sciences, especially management research.The aim is to facilitate management science readers' understanding of how grey systems theory can be applied in research, providing specific guidance on the use of grey methods in management studies and discussing their possibilities and limitations. This work provides methodological guidelines for conducting research in management science.The primary audience includes management science researchers and doctoral students dealing with empirical research where incomplete and sparse data are a critical factor, and scholars in the grey systems theory community. The book introduces new concepts from the axiomatisation of greyness, appealing to specialists interested in this area.

This book aims to present an overview of grey system models for time series modelling and forecasting. It is about modelling and forecasting time series with ordinary differential equations, especially when the available samples are extremely limited. Grey system models (GSM) develop sequence operators to nonparametrically identify the underlying dynamics from the limited observations. This book concerns about two important modelling themes, small sample and poor information. The former focuses on the mechanism and methodology of GSMs for small-sample real-number time series, and the latter on the uncertainty quantification of grey number together with its small-sample modelling principles. In this book, a broad entry point to applied data science for students majoring in economic, management science, and engineering is applied, covering a wide range of topics from basic introductory material up to research-level techniques.

This book aims to present an overview of grey system models for time series modelling and forecasting. It is about modelling and forecasting time series with ordinary differential equations, especially when the available samples are extremely limited. Grey system models (GSM) develop sequence operators to nonparametrically identify the underlying dynamics from the limited observations. This book concerns about two important modelling themes, small sample and poor information. The former focuses on the mechanism and methodology of GSMs for small-sample real-number time series, and the latter on the uncertainty quantification of grey number together with its small-sample modelling principles. In this book, a broad entry point to applied data science for students majoring in economic, management science, and engineering is applied, covering a wide range of topics from basic introductory material up to research-level techniques.

This book introduces the grey prediction model methodology and its applications as a professional book. It involves the key concepts, data characteristics of modelling objects and the latest research achievements in grey system. The grey prediction models for homogeneous and non-homogeneous exponential sequences, saturated S-shaped sequences and special sequences have been introduced.  This book combines the classical models and the latest models, case studies and model applications, modelling methods, and MATLAB programs in grey system. It is a guidebook for both new learners and professionals of grey prediction models.

This book introduces the grey prediction model methodology and its applications as a professional book. It involves the key concepts, data characteristics of modelling objects and the latest research achievements in grey system. The grey prediction models for homogeneous and non-homogeneous exponential sequences, saturated S-shaped sequences and special sequences have been introduced.  This book combines the classical models and the latest models, case studies and model applications, modelling methods, and MATLAB programs in grey system. It is a guidebook for both new learners and professionals of grey prediction models.

This open access book is the 2nd edition involving the update on data, methods and models of Grey Systems. It covers up-to-date theoretical and applied advances in grey systems from across the world, and vividly presents the reader with the overall picture of this new theory and its frontier research. Many of the concepts, models and methods in the book are original by the author, including kernel, degree of greyness of grey number, simplified form of grey number, general grey number and the operation system; the axiomatic system of buffer operators and a series of weakening and strengthening buffer operators; a series of grey relational analysis models, including grey absolute, relative, synthetic, similarity, closeness, negative, three dimension, and grey relational analysis model for cross-sequences, etc.; grey fixed weight clustering model, grey evaluation models based on center-point and end-point mixed possibility functions; original difference grey model (ODGM), even difference grey model (EDGM), discrete grey model (DGM), fractional grey models, self-memory grey models; multi-attribute weighted intelligent grey target decision models, kernel weight vector group and the weighted comprehensive clustering coefficient vector, and spectrum analysis of sequence operators, etc.The revision includes: (1) Added new achievements made in recent years, such as the moving average denoise operator, a series of negative grey relational models, grey relational model for cross-sequences, standard uncertainty numbers and their operations, adaptive Grey Prediction Models and so on; (2) Important data related to the development of grey system theory has been updated; (3) Research reviews have been added to each chapter, and a large number of references have been added; (4)Updated application examples of commonly used models and methods.This book will be appropriate as a reference and/or textbook for courses of grey system theory for graduate students or high level undergraduate students, majoring in various fields of natural sciences, social sciences and engineering technology. It can also be utilized by researchers and technicians in research institutions, business entities, and government agencies.