Multivariate Discrete Grey Model base on Dummy Drivers

Zhang, Ke; Pinpin, Qu

doi:10.1109/gsis.2015.7301866

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…GM (1, N) model, in spite of successfully applying in various fields, sometimes ignores the influence of virtual variables such as policy on the main system in practical applications. Zhang (2016) considered the influence of dummy variables on system behavior variables and built a discrete multivariable prediction model based on dummy variables, which further expanded the application scope of the model. Ding et al (2018) introduced the dummy variables into the GM (1, N) model, gave the concrete model construction method in mathematics and verified the effectiveness of the new model with cases.…”

Section: Gsmentioning

confidence: 99%

Forecasting China's energy intensity by using an improved DVCGM (1, N) model considering the hysteresis effect

Meng

Xiaotong

Yin

et al. 2020

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PurposeThe purpose of this paper is to examine the effectiveness of an improved dummy variables control grey model (DVCGM) considering the hysteresis effect of government policies in China's energy intensity (EI) forecasting.Design/methodology/approachEnergy consumption is considered as an important driver of economic development. China has introduced policies those aim at the optimization of energy structure and EI. In this study, EI is forecasted by an improved DVCGM, considering the hysteresis effect of energy-saving policies of the government. A nonlinear optimization method based on particle swarm optimization (PSO) algorithm is constructed to calculate the hysteresis parameter. A one-step rolling mechanism is applied to provide input data of the prediction model. Grey model (GM) (1, N), DVCGM (1, N) and ARIMA model are applied to test the accuracy of the improved DVCGM (1, N) model prediction.FindingsThe results show that the improved DVCGM provides reliable results and works well in simulation and predictions using multivariable data in small sample size and time-lag virtual variable. Accordingly, the improved DVCGM notes the hysteresis effect of government policies and significantly improves the prediction accuracy of China's EI than the other three models.Originality/valueThis study estimates the EI considering the hysteresis effect of energy-saving policies in China by using an improved DVCGM. The main contribution of this paper is to propose a model to estimate EI, considering the hysteresis effect of energy-saving policies and improve forecasting accuracy.

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Section: Gsmentioning

confidence: 99%

Forecasting China's energy intensity by using an improved DVCGM (1, N) model considering the hysteresis effect

Meng

Xiaotong

Yin

et al. 2020

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“…In previous traditional Grey studies, the value of ζ was always set as 0.5 [28]- [30]. There is a certain source of error for background value calculation, which uses right-angle trapezoid formula instead of the curved-edge trapezoid.…”

Section: B Fractional Nonlinear Grey Bernoulli Modelmentioning

confidence: 99%

A Novel Mutual Fractional Grey Bernoulli Model With Differential Evolution Algorithm and Its Application in Education Investment Forecasting in China

Xie

Pei³

et al. 2020

IEEE Access

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Nowadays, the nonlinear grey Bernoulli model [NGBM(1,1)] has been successfully applied to various fields. Its main advantage is that the power exponent can better reflect the non-linear characteristics of the original data. However, the parameters of the model (i.e., the order of accumulation, coefficient of background value, and power index) must be optimized to fit the development law of the system. In this study, a fractional non-linear grey Bernoulli model [MFNGBM (1,1)] is proposed to reduce the perturbation limit of the classical NGBM and further improve the accuracy of the model, which uses mutual fractional operators and a new optimization scheme with a differential evolution (DE) algorithm for forecasting education investment. In the scheme, the power exponent of the Bernoulli differential equation, coefficient of background, and cumulative order of the original sequence are taken as decision variables, and their optimal parameters obtained by iteratively adjusting fitness functions. The experimental evaluation is conducted on two types of open-source data, and the results show that the proposed method can be very competitive with the popular baselines. Finally, MFNGBM(1,1) is used to predict China's education investment in 2020-2025. INDEX TERMS Grey system, grey forecasting model, non-linear grey bernoulli model, fractional order, differential evolution algorithm, educational investment.

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“…For example, the use of the most widely used grey model GM (1,1) (Liu and Xie, 2015) requires only four raw data to be modeled and predicted. Because of the advantages of simple algorithm and high prediction accuracy, GM (1,1) model and its expanded form have been applied in many practical applications in recent years (Cui et al, 2014;Liu et al, 2015;Kapila Tharanga Rathnayaka et al, 2016;Zhang, 2016). This paper will be based on the actual data of the new energy industry and its five sub-industries in China from 2003 to 2013.…”

Section: Introductionmentioning

confidence: 99%

Export injury early warning of the new energy industries in China

Wang

2017

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Purpose In recent years, continuous expansion of the scale of the new energy export industry in China caused a boycott of American and European countries. Export injury early warning research is an urgent task to develop the new energy industry in China. The purpose of this paper is to build an indicator system of exports injury early warning of the new energy industry in China and corresponding quantitative early warning models. Design/methodology/approach In consideration of the actual condition of the new energy industry in China, this paper establishes an indicator system according to four aspects: export price, export quantity, impact on domestic industry and impact on macro economy. Based on the actual data of new energy industry and its five sub-industries (solar, wind, nuclear power, smart grid and biomass) in China from 2003 to 2013, GM (1,1) model is used to predict early warning index values for 2014-2018. Then, the principal component analysis (PCA) is used to obtain the comprehensive early warning index values for 2003-2018. The 3-sigma principle is used to divide the early warning intervals according to the comprehensive early warning index values for 2003-2018 and their standard deviation. Finally, this paper determines alarm degrees for 2003-2018. Findings Overall export condition of the new energy industry in China is a process from cold to normal in 2003-2013, and the forecast result shows that it will be normal from 2014 to 2018. The export condition of the solar energy industry experienced a warming process, tended to be normal, and the forecast result shows that it will also be normal in 2014-2018. The biomass and other new energy industries and nuclear power industry show a similar development process. Export condition of the wind energy industry is relatively unstable, and it will be partially hot in 2014-2018, according to the forecast result. As for the smart grid industry, the overall export condition of it is normal, but it is also unstable, in few years it will be partially hot or partially cold. The forecast result shows that in 2014-2018, it will maintain the normal state. In general, there is a rapid progress in the export competitiveness of the new energy industry in China in the recent decade. Practical implications Export injury early warning research of the new energy industry can help new energy companies to take appropriate measures to reduce trade losses in advance. It can also help the relevant government departments to adjust industrial policies and optimize the new energy industry structure. Originality/value This paper constructs an index system that can measure the alarm degrees of the new energy industry. By combining the GM (1,1) model and the PCA method, the problem of warning condition detection under small sample data sets is solved.

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Multivariate Discrete Grey Model base on Dummy Drivers

Cited by 4 publications

References 11 publications

Forecasting China's energy intensity by using an improved DVCGM (1, N) model considering the hysteresis effect

Forecasting China's energy intensity by using an improved DVCGM (1, N) model considering the hysteresis effect

A Novel Mutual Fractional Grey Bernoulli Model With Differential Evolution Algorithm and Its Application in Education Investment Forecasting in China

Export injury early warning of the new energy industries in China

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