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Article
Publication date: 25 January 2013

Zhang ke

The purpose of this paper is to establish a random simulation method to compare the forecasting performance between grey prediction models, and between grey model and…

Abstract

Purpose

The purpose of this paper is to establish a random simulation method to compare the forecasting performance between grey prediction models, and between grey model and other kinds of prediction models. Then, the different performance of three grey models and linear regression prediction model is studied, based on the proposed method.

Design/methodology/approach

A random simulation method was proposed to test the modelling accuracy of grey prediction model. This method was enlightened by Monte Carlo simulation method. It regarded a class of sequences as population, and selected a large sample from population though random sampling. Then, sample sequences were modeled by grey prediction model. Through modeling error calculation, the average error of grey model for the sample was obtained. Finally, the grey model accuracy for this kind of problem was acquired by statistical inference testing model. Through the statistical significant test method, the modeling accuracy of grey models for the same problem can be compared. Also, accuracy difference between grey prediction model and regression analysis, support vector machine, neural network, and other forecasting methods can be also compared.

Findings

Though random simulation experiments, the following conclusion was obtained. First, grey model can be applied to the long sequence whose growth rate was less than 20 per cent, and the short sequence whose growth rate was less than 50 per cent. Second, GM(1,1) cannot be applied to a long sequence with high growth. Third, growth rate was a more important factor than growth length on modeling accuracy of GM(1,1). Fourth, when the sequence length was short, accuracy of GM(1,1) model was higher than linear regression. While the length of the sequence was more than 15, and the growth rate in [0‐10 per cent], two kinds of modeling error was not significantly different.

Practical implications

The method proposed in the paper can be used to compare the performance of different prediction models, and to select appropriate model for a prediction problem.

Originality/value

The paper succeeded in establishing an accuracy test method for grey models and other prediction models. It will standardize the grey modelling and contribute to application of grey models.

Details

Grey Systems: Theory and Application, vol. 3 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 6 July 2021

Xu Peng, Xiang Li and Xiao Yang

In order to more accurately predict the dynamics of the e-commerce market and increase the comprehensive value of the circular e-commerce industry, proposes to use Grey

Abstract

Purpose

In order to more accurately predict the dynamics of the e-commerce market and increase the comprehensive value of the circular e-commerce industry, proposes to use Grey system theory to analyze the circular economy of the e-commerce market.

Design/methodology/approach

Construct a Grey system theory model, analyze the big data of e-commerce and circular economy of the e-commerce market and predict the development potential of China's e-commerce market.

Findings

The results show that the Grey system theory model can play an important role in the data analysis of circular economy of the e-commerce market.

Originality/value

Use Grey model to analyze e-commerce data, discover e-commerce market rules and problems and then optimize e-commerce market.

Details

Journal of Enterprise Information Management, vol. 35 no. 4/5
Type: Research Article
ISSN: 1741-0398

Keywords

Article
Publication date: 6 February 2017

Asli Özdemir and Güzin Özdagoglu

Prediction problems raised in uncertain environments require different solution approaches such as grey prediction models, which consider uncertainty in information and…

Abstract

Purpose

Prediction problems raised in uncertain environments require different solution approaches such as grey prediction models, which consider uncertainty in information and also enable the use of small data sets. The purpose of this paper is to investigate the comparative performances of grey prediction models (GM) and Markov chain integrated grey models in a demand prediction problem.

Design/methodology/approach

The modeling process of grey models is initially described, and then an integrated model called the Grey-Markov model is presented for the convenience of applications. The analyses are conducted on a monthly demand prediction problem to demonstrate the modeling accuracies of the GM (1,1), GM (2,1), GM (1,1)-Markov, and GM (2,1)-Markov models.

Findings

Numerical results reveal that the Grey-Markov model based on GM (2,1) achieves better prediction performance than the other models.

Practical implications

It is thought that the methodology and the findings of the study will be a significant reference for both academics and executives who struggle with similar demand prediction problems in their fields of interest.

Originality/value

The novelty of this study comes from the fact that the GM (2,1)-Markov model has been first used for demand prediction. Furthermore, the GM (2,1)-Markov model represents a relatively new approach, and this is the second paper that addresses the GM (2,1)-Markov model in any area.

Details

Grey Systems: Theory and Application, vol. 7 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Open Access
Article
Publication date: 22 October 2019

Li Xuemei, Yun Cao, Junjie Wang, Yaoguo Dang and Yin Kedong

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of…

1753

Abstract

Purpose

Research on grey systems is becoming more sophisticated, and grey relational and prediction analyses are receiving close review worldwide. Particularly, the application of grey systems in marine economics is gaining importance. The purpose of this paper is to summarize and review literature on grey models, providing new directions in their application in the marine economy.

Design/methodology/approach

This paper organized seminal studies on grey systems published by Chinese core journal database – CNKI, Web of Science and Elsevier from 1982 to 2018. After searching the aforementioned database for the said duration, the authors used the CiteSpace visualization tools to analyze them.

Findings

The authors sorted the studies according to their countries/regions, institutions, keywords and categories using the CiteSpace tool; analyzed current research characteristics on grey models; and discussed their possible applications in marine businesses, economy, scientific research and education, marine environment and disasters. Finally, the authors pointed out the development trend of grey models.

Originality/value

Although researches are combining grey theory with fractals, neural networks, fuzzy theory and other methods, the applications, in terms of scope, have still not met the demand. With the increasingly in-depth research in marine economics and management, international marine economic research has entered a new period of development. Grey theory will certainly attract scholars’ attention, and its role in marine economy and management will gain considerable significance.

Details

Marine Economics and Management, vol. 2 no. 2
Type: Research Article
ISSN: 2516-158X

Keywords

Article
Publication date: 14 June 2019

Pingping Xiong, Zhiqing He, Shiting Chen and Mao Peng

In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to…

Abstract

Purpose

In recent years, domestic smog has become increasingly frequent and the adverse effects of smog have increasingly become the focus of public attention. It is a way to analyze such problems and provide solutions by mathematical methods.

Design/methodology/approach

This paper establishes a new gray model (GM) (1,N) prediction model based on the new kernel and degree of grayness sequences under the case that the interval gray number distribution information is known. First, the new kernel and degree of grayness sequences of the interval gray number sequence are calculated using the reconstruction definition of the kernel and degree of grayness. Then, the GM(1,N) model is formed based on the above new sequences to simulate and predict the kernel and degree of the grayness of the interval gray number sequence. Finally, the upper and lower bounds of the interval gray number are deduced based on the calculation formulas of the kernel and degree of grayness.

Findings

To verify further the practical significance of the model proposed in this paper, the authors apply the model to the simulation and prediction of smog. Compared with the traditional GM(1,N) model, the new GM(1,N) prediction model established in this paper has better prediction effect and accuracy.

Originality/value

This paper improves the traditional GM(1,N) prediction model and establishes a new GM(1,N) prediction model in the case of the known distribution information of the interval gray number of the smog pollutants concentrations data.

Details

Kybernetes, vol. 49 no. 3
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 6 January 2022

Wuyong Qian, Hao Zhang, Aodi Sui and Yuhong Wang

The purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for…

Abstract

Purpose

The purpose of this study is to make a prediction of China's energy consumption structure from the perspective of compositional data and construct a novel grey model for forecasting compositional data.

Design/methodology/approach

Due to the existing grey prediction model based on compositional data cannot effectively excavate the evolution law of correlation dimension sequence of compositional data. Thus, the adaptive discrete grey prediction model with innovation term based on compositional data is proposed to forecast the integral structure of China's energy consumption. The prediction results from the new model are then compared with three existing approaches and the comparison results indicate that the proposed model generally outperforms existing methods. A further prediction of China's energy consumption structure is conducted into a future horizon from 2021 to 2035 by using the model.

Findings

China's energy structure will change significantly in the medium and long term and China's energy consumption structure can reach the long-term goal. Besides, the proposed model can better mine and predict the development trend of single time series after the transformation of compositional data.

Originality/value

The paper considers the dynamic change of grey action quantity, the characteristics of compositional data and the impact of new information about the system itself on the current system development trend and proposes a novel adaptive discrete grey prediction model with innovation term based on compositional data, which fills the gap in previous studies.

Details

Grey Systems: Theory and Application, vol. 12 no. 3
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 3 October 2016

Hui-Wen Vivian Tang and Tzu-chin Rojoice Chou

The purpose of this paper is to evaluate the forecasting performance of grey prediction models on educational attainment vis-à-vis that of exponential smoothing combined…

Abstract

Purpose

The purpose of this paper is to evaluate the forecasting performance of grey prediction models on educational attainment vis-à-vis that of exponential smoothing combined with multiple linear regression employed by the National Center for Education Statistics (NCES).

Design/methodology/approach

An out-of-sample forecasting experiment was carried out to compare the forecasting performances on educational attainments among GM(1,1), GM(1,1) rolling, FGM(1,1) derived from the grey system theory and exponential smoothing prediction combined with multivariate regression. The predictive power of each model was measured based on MAD, MAPE, RMSE and simple F-test of equal variance.

Findings

The forecasting efficiency evaluated by MAD, MAPE, RMSE and simple F-test of equal variance revealed that the GM(1,1) rolling model displays promise for use in forecasting educational attainment.

Research limitations/implications

Since the possible inadequacy of MAD, MAPE, RMSE and F-type test of equal variance was documented in the literature, further large-scale forecasting comparison studies may be done to test the prediction powers of grey prediction and its competing out-of-sample forecasts by other alternative measures of accuracy.

Practical implications

The findings of this study would be useful for NCES and professional forecasters who are expected to provide government authorities and education policy makers with accurate information for planning future policy directions and optimizing decision-making.

Originality/value

As a continuing effort to evaluate the forecasting efficiency of grey prediction models, the present study provided accumulated evidence for the predictive power of grey prediction on short-term forecasts of educational statistics.

Details

Kybernetes, vol. 45 no. 9
Type: Research Article
ISSN: 0368-492X

Keywords

Article
Publication date: 9 August 2022

Bingjun Li, Shuhua Zhang, Wenyan Li and Yifan Zhang

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985…

Abstract

Purpose

Grey modeling technique is an important element of grey system theory, and academic articles applied to agricultural science research have been published since 1985, proving the broad applicability and effectiveness of the technique from different aspects and providing a new means to solve agricultural science problems. The analysis of the connotation and trend of the application of grey modeling technique in agricultural science research contributes to the enrichment of grey technique and the development of agricultural science in multiple dimensions.

Design/methodology/approach

Based on the relevant literature selected from China National Knowledge Infrastructure, the Web of Science, SpiScholar and other databases in the past 37 years (1985–2021), this paper firstly applied the bibliometric method to quantitatively visualize and systematically analyze the trend of publication, productive author, productive institution, and highly cited literature. Then, the literature is combed by the application of different grey modeling techniques in agricultural science research, and the literature research progress is systematically analyzed.

Findings

The results show that grey model technology has broad prospects in the field of agricultural science research. Agricultural universities and research institutes are the main research forces in the application of grey model technology in agricultural science research, and have certain inheritance. The application of grey model technology in agricultural science research has wide applicability and precise practicability.

Originality/value

By analyzing and summarizing the application trend of grey model technology in agricultural science research, the research hotspot, research frontier and valuable research directions of grey model technology in agricultural science research can be more clearly grasped.

Details

Grey Systems: Theory and Application, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 11 June 2020

Ye Li, Sandang Guo and Juan Li

The purpose of this paper is to construct a prediction model of three-parameter interval grey number based on kernel and double information domains to expand the modeling…

Abstract

Purpose

The purpose of this paper is to construct a prediction model of three-parameter interval grey number based on kernel and double information domains to expand the modeling object of grey prediction model from interval grey number to three-parameter interval grey number.

Design/methodology/approach

First, the study decomposes the grey valued interval into upper and lower cells with the “center of gravity” as the dividing point and defines the upper and lower information domains of the three-parameter interval grey number. Second, it calculates the kernel, the upper and lower information domains of the three-parameter interval grey number. Then, it constructs the prediction model for kernel sequence and upper and lower information domain sequences, respectively. By deducing the time response expressions of “center of gravity”, lower and upper limits of three-parameter interval grey number, a prediction model of three-parameter interval grey number based on kernel and double information domains is obtained.

Findings

This paper provides a prediction model of three-parameter interval grey number based on kernel and double information domains, and the example analysis shows that the method proposed in this paper has higher prediction accuracy and practicality.

Practical implications

In this paper, the modeling object of grey prediction model is extended to the three-parameter interval grey number, so it can be used for the prediction of uncertainty problems, such as stock changing trend, temperature and so on.

Originality/value

By decomposing the grey valued interval into upper and lower cells with the “center of gravity” as the dividing point, gives the definition of upper and lower information domains and then obtains a new method for whitening the three-parameter interval grey number.

Details

Grey Systems: Theory and Application, vol. 10 no. 4
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 17 August 2012

Huang Chang Mei, Shen Wei Hua and Xiao Xiao Cong

The paper attempts to establish GM(1,1) grey prediction model group for the top three Olympic track and field sports performance, and to predict the 30th London Olympic…

Abstract

Purpose

The paper attempts to establish GM(1,1) grey prediction model group for the top three Olympic track and field sports performance, and to predict the 30th London Olympic track and field results and its tendency using grey systems theory.

Design/methodology/approach

Athletics sports achievements are influenced by many factors, such as the physical quality, athletes individual growth cycle, and injuring or retirement of excellent athletes, the outstanding performance of some athletes, the using of high‐tech sports training instrument, the implementation plan of scientific training guidance, the introduction of advanced technology, facilities and improvement, and so on. Those aspects can make the match result uncertain, which are running in a uncertain and continually changing environment, so sports achievements have obviously grey features. Combined with grey modeling methods, and aimed at the top three Olympic track and field sports performance, this paper established GM (1,1) grey prediction model group and analysed the trend of Olympic track and field. And in the end of the paper, the 30th Olympic men's and women's the top three athletic achievements prediction intervals are also predicted.

Findings

The results show that forecasting model group has high‐precision. In the 46 champions prediction models, three models have the forecast accuracy of 100 percent; 27 models' forecast accuracy are greater than 99.5 percent, and the rest of the models forecast accuracy are greater than 98.58 percent. In the 46 silver medalists prediction models, five models have the forecast accuracy of 100 percent; 33 models' forecast accuracy are greater than 99.5 percent and the rest of the models' forecast accuracy is greater than 98.48 percent. In the 46 bronze medalist prediction models, four models have the forecast accuracy of 100 percent; 25 models' forecast accuracy is greater than 99.5 percent and the rest of the models forecast accuracy is greater than 98.76 percent. The essay deeply analyzes the top three achievements' trend of Olympic Games Track and field. In the end, the paper predicts the 30th Olympic track and field results.

Practical implications

The method exposed in the paper can be used for the short‐term or long‐term prediction of sports scores metering in international competition (such as track and field, swimming, rowing, etc.), and also for personal athletic performance prediction.

Originality/value

The paper succeeds in realising both grey prediction model group for the top three Olympic track and field performance in all projects, and prediction of the 30th London Olympic track and field results by using the newest developed theories: grey systems theory.

Details

Grey Systems: Theory and Application, vol. 2 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

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