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Grey Forecasting Mechanism, Models and Applications

Title
Grey Forecasting [electronic resource] : Mechanism, Models and Applications / by Naiming Xie, Baolei Wei.
ISBN
9789819753239
Edition
1st ed. 2024.
Publication
Singapore : Springer Nature Singapore : Imprint: Springer, 2024.
Physical Description
1 online resource (XIII, 200 p.) 24 illus., 15 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
September 11, 2024
Series
Series on Grey System,
Series on Grey System,
Contents
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.
Citation

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