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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. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-0398

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…

1669

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: 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: 4 September 2019

Erkan Kose and Levent Tasci

The purpose of this paper is to examine the effectiveness of the multivariable grey prediction model in deformation forecasting.

Abstract

Purpose

The purpose of this paper is to examine the effectiveness of the multivariable grey prediction model in deformation forecasting.

Design/methodology/approach

Deformation in a dam can be seen because of many factors but without any doubt, the most influential factor is the water level. In this study, the deformation level of a point in the Keban Dam crest has been tried to be forecasted depending on the water level by the multivariable grey model GM(1,N). Regression analysis was used to test the accuracy of the prediction results obtained using the grey prediction model.

Findings

The results show that there is a great consistency between the grey prediction values and the actual values, and that the GM(1,N) produces more reliable results than the regression analysis. Based on the results, it can be concluded that the GM(1,N) is a very reliable estimation model for limited data conditions.

Originality/value

Different from the other studies in the literature, this study investigates deformation in a dam subject to the water level in the dam reservoir. The main contribution of the study to the literature is to suggest a relatively new procedure for estimating the deformation in the dams based on the water level.

Details

Grey Systems: Theory and Application, vol. 9 no. 4
Type: Research Article
ISSN: 2043-9377

Keywords

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: 29 July 2014

Pinpin Qu

The mobile communication industry in China is vulnerable to competition, industry regulation, macroeconomy and so on, which leads to service income's volatility and

Abstract

Purpose

The mobile communication industry in China is vulnerable to competition, industry regulation, macroeconomy and so on, which leads to service income's volatility and non-stationarity. Traditional income prediction models fail to take account of these factors, thus resulting in a low precision. The purpose of this paper is to to set up a new mobile communication service income prediction model based on grey system theory to overcome the inconformity between traditional models and qualitative analysis.

Design/methodology/approach

At first, mobile telecommunication service income is divided into number of users (NU) and average revenue per user (ARPU) prediction, respectively. Then, grey buffer operators are introduced to preprocess the time series according to their features and tendencies to eliminate the effect of shock disturbance. As a result, two grey models based on GM(1, 1) are constructed to forecast NU and ARPU, and thus the service income is obtained. At last, a case on Zhujiang mobile communication company is studied. The result proves that the proposed method is not only more accurate, but also could discover the turning point of income.

Findings

The results are convincing: it is more effective and accurate to employ grey buffer operator theory to predict the mobile communication service income compared with other methods. Besides, this method is applicable to cases with less data samples and faster development.

Practical implications

It's common to come across a system with less data and poor information. At this case, the grey prediction method exposed in the paper can be used to forecast the future trend which will give the predictors advice to achieve fine outcomes. Buffer operators can reduce the effect of shock disturbance and the GM(1, 1) model has the advantages of exploiting information using only a couple of data.

Originality/value

Considering the fast development of China's mobile communication in recent years, only limited data can be acquired to predict the future, which will definitely reduce the prediction precision using traditional models. The paper succeeds in introducing GM(1, 1) model based on grey buffer operators into the income prediction and the outcome proves that it has higher prediction precision and extensive application.

Details

Grey Systems: Theory and Application, vol. 4 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 29 March 2019

Zhang Lixia, Tang Hong and He Miao

The purpose of this paper is to predict hospital respiratory system infection rate by using the gray GM(1,1) model, and to provide theoretical basis for the prospective…

Abstract

Purpose

The purpose of this paper is to predict hospital respiratory system infection rate by using the gray GM(1,1) model, and to provide theoretical basis for the prospective study on hospital respiratory system infection management.

Design/methodology/approach

The annual respiratory system infection rate of a comprehensive third-class hospital in Yan’an is collected from 2011 to 2017. The GM(1,1) model is used for prediction, and mean absolute percentage error is used to evaluate the prediction accuracy of the model.

Findings

GM(1,1) statistical prediction model is established with good fitting degree and high reliability of extrapolation prediction.

Originality/value

The GM(1,1) model can well predict the respiratory system infection rate of the hospital.

Details

Grey Systems: Theory and Application, vol. 9 no. 2
Type: Research Article
ISSN: 2043-9377

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: 17 August 2012

Deborah Lim, Patricia Anthony and Ho Chong Mun

As the demand for online auctions increases, the process of monitoring multiple auction houses, deciding which auction to participate in and making the right bids, become…

Abstract

Purpose

As the demand for online auctions increases, the process of monitoring multiple auction houses, deciding which auction to participate in and making the right bids, become challenging tasks for consumers. Hence, knowing the closing price of a given auction would be an advantage, since this information will ensure a win in a given auction. However, predicting a closing price for an auction is not easy, since it is dependent on many factors. The purpose of this paper is to report on a predictor agent that utilises grey system theory to predict the closing price for a given auction.

Design/methodology/approach

The focus of the research is on grey system agent. This paper reports on the development of a predictor agent that attempts to predict the online auction closing price in order to maximise the bidder's profit. The performance of this predictor agent is compared with two well‐known techniques, the Simple Exponential Function and the Time Series, in a simulated auction environment and in the eBay auction.

Findings

The grey theory agent gives a better result when less input data are made, while the Time Series Agent can be used with the availability of a lot of information. Although the Simple Exponential Function Agent is able to predict well with less input data, it is not an appropriate method to be applied in the prediction model since its formula is not realistic and applicable in predicting the online auction closing price. The experimental results also showed that using moving historical data produces a higher accuracy rate than using fixed historical data for all three agents.

Originality/value

Grey system theory prediction model, GM(1, 1) has not been applied in online auction prediction. In this paper the authors have applied grey theory into an agent to predict the closing price of an online auction, in order to increase the profit of bidders in the bidding stage. The experimental results show that the accuracy of the grey prediction model is more then 90 per cent, with less then eight historical data inputs.

Details

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

Keywords

Article
Publication date: 10 October 2007

Guo‐Dong Li, Daisuke Yamaguchi and Masatake Nagai

This paper aims to increase the manufacturing accuracy and quality of product by improving the prediction accuracy of forecasting compensatory control (FCC).

395

Abstract

Purpose

This paper aims to increase the manufacturing accuracy and quality of product by improving the prediction accuracy of forecasting compensatory control (FCC).

Design/methodology/approach

The dynamic analysis model, which combines grey dynamic model with time series autoregressive integrated moving average (ARIMA) model is proposed. In addition, the Markov chain from stochastic process theory is applied to improve the prediction accuracy.

Findings

The proposed model is more accurate than ARIMA model and grey dynamic model.

Originality/value

The paper provides a viewpoint on FCC by using the combined methodology, which takes advantage of high predictable power of grey dynamic model and at the same time takes advantage of the prediction powers of ARIMA model and Markov chain.

Details

Engineering Computations, vol. 24 no. 7
Type: Research Article
ISSN: 0264-4401

Keywords

1 – 10 of over 4000