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Grey forecasting = mechanism, models and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Grey forecasting/ by Naiming Xie, Baolei Wei.
Reminder of title:	mechanism, models and applications /
Author:	Xie, Naiming.
other author:	Wei, Baolei.
Published:	Singapore :Springer Nature Singapore : : 2024.,
Description:	xiii, 200 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Time-series analysis. -
Online resource:	https://doi.org/10.1007/978-981-97-5323-9
ISBN:	9789819753239

Grey forecasting = mechanism, models and applications /
Xie, Naiming.

Grey forecastingmechanism, models and applications /[electronic resource] :by Naiming Xie, Baolei Wei. - Singapore :Springer Nature Singapore :2024. - xiii, 200 p. :ill., digital ;24 cm. - Series on grey system,2731-4944. - Series on grey system..

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.

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.

ISBN: 9789819753239

Standard No.: 10.1007/978-981-97-5323-9doiSubjects--Topical Terms:

532530
Time-series analysis.

LC Class. No.: QA280

Dewey Class. No.: 519.55

Grey forecasting = mechanism, models and applications /
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505 0 $a 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.
520 $a 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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