SARIMA damp trend grey forecasting model for airline industry

Purpose – The purpose of this paper is to propose an economic cybernetics model based on the grey differential equation GM(1,N) for China's high-tech industries and provide the necessary support to assist high-tech industries management departments with their policy making. Design/methodology/approach – Based on the principle of grey differential equation GM(1,N), the grey differential equations of five high-tech industries in China are established using the net fixed assets, labor quantity and patent application quantity as cybernetics variables. After the discretization and first-order subtraction reduction to the simultaneous equation of the five grey models, a linear cybernetics model is resulted in. The structure parameters in the cybernetics system show explicit economic significance and can be identified through least square principle. At last, the actual data in 2004-2010 are introduced to empirically analyze the high-tech industrial system in China. Findings – The cybernetics system for China's h...

A GM(1,N)-based economic cybernetics model for the high-tech industries in China

Non-equidistant step by step optimum new information GM(1,1) and its application

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

Geodetic deformation forecasting based on multi-variable grey prediction model and regression model

The grey dynamic model by convolution integral with the first-order derivative of the 1-AGO data and series related, abbreviated as GDMC, performs well in modelling and forecasting of a grey system. To improve the modelling accuracy of GDMC, interpolation coefficients (taken as unknown parameters) are introduced into the background values of the variables. The parameters optimization is formulated as a combinatorial optimization problem and is solved collectively using the particle swarm optimization algorithm. The optimized result has been verified by a case study of the economic output of high-tech industry in China. Comparisons of the obtained modelling results from the optimized GDMC model with the traditional one demonstrate that the optimal algorithm is a good alternative for parameters optimization of the GDMC model. The modelling results can assist the government in developing future policies regarding high-tech industry management.

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An Optimized Grey Dynamic Model for Forecasting the Output of High-Tech Industry in China

Purpose – The purpose of this paper is to overcome the deficiency of the current GM(1,N) such as low‐prediction precision, extend the scope of GM(1,N) and provide an effective grey dynamic model GM(1,N) for the relationship of cost and variability.Design/methodology/approach – The relationship between two factors of variety and the cost of manufacturing system is studied on the basis of the variety reduction program theory. Based on the Grey system and the gradient algorithm, a Grey dynamic model GM(1,N) is proposed between cost and variety by optimizing the coefficient and background value of the model which is used to check validity for the relation of plasm‐yarn machine product and variety.Findings – The proposed Grey dynamic prediction model GM(1,N) for the relationship of cost and variability has high precision and easy‐to‐use.Research limitations/implications – A Grey model GM(1,N) for prediction is proposed.Practical implications – The proposed model should also have potential for multifactor syste...

Grey dynamic model GM(1,N) for the relationship of cost and variability

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Grey dynamic model GM(1,N) for the relationship of cost and variability