Grey Forecasting
Description
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.
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Persons
Naiming Xie is a full professor at Institute of Grey System Studies, Nanjing University of Aeronautics and Astronautics. He graduated at Nanjing University of Aeronautics and Astronautics (NUAA) and got Ph.D. degree in 2008. His major research areas are grey system theory, uncertainty scheduling, and data analytics. He has published 5 books and more than 140 journal articles, more than 90 of them were indexed in SCI/SSCI. He took in charge of nearly 20 scientific research projects, including 4 projects funded by National Science Foundation of China (NSFC) and 1 jointed funding by NSFC and Royal Society of UK. Prof Xie is an associate editor of the international journal, Grey Systems: Theory and Application since 2011. And Prof. Xie is also the secretary-general of International Association of Grey System and Uncertainty Analysis and the vice president of Grey System Professional Committee of Overall Planning and Economical Mathematics (China).
Content
1. Introduction.- 2. Architecture of grey forecasting models.- 3. Mechanism and reduction of grey forecasting models.- 4. Continuous-time grey endogenous forecasting models.- 5. Discrete-time grey endogenous forecasting models.- 6. Continuous-time grey exogenous forecasting models.- 7. Discrete-time grey exogenous forecasting models.- 8. Nonlinear grey forecasting models.- 9. Grey number-oriented forecasting models.- 10. Comments and future thinking.