Analysing the high-tech industry with a multivariable grey forecasting model based on fractional order accumulation

Kybernetes ◽  
2019 ◽  
Vol 48 (6) ◽  
pp. 1158-1174 ◽  
Author(s):  
Liang Zeng
Keyword(s):  
Prediction Model ◽  
Fractional Order ◽  
Guangdong Province ◽  
High Tech ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Background Value ◽  
Grey Forecasting ◽  
Optimized Model

PurposeHigh-tech industries play an important role in promoting economic and social development. The purpose of this paper is to accurately predict and analyze the output value of high-tech products in Guangdong Province, China, by using a multivariable grey model.Design/methodology/approachBased on the principle of fractional order accumulation, this study proposes a multivariable grey prediction model. To further enhance the prediction ability and accuracy of the model, an optimized model is established by reconstructing the background value. The optimal parameters are solved by minimizing the average relative error of the system characteristic sequence with the constraint of parameter relationships.FindingsThe results from the study show that the two proposed models exhibit better simulation and prediction performance than the traditional models, while the optimized model can significantly improve the modelling precision. In addition, it is predicted that the output value of high-tech products is 12,269.443bn yuan in 2021, which will approximately double from 2016 to 2021.Research limitations/implicationsThe two proposed models can be used to forecast the trend of the system and are grown as an effective extension and supplement of the traditional multivariable grey forecasting models.Practical implicationsThe forecast and analysis of the development prospects of high-tech industries would be useful for the government departments of Guangdong Province and professional forecasters to grasp the future of high-tech industries and formulate decision planning.Originality/valueA new multivariable grey prediction model based on fractional order accumulation and its optimized model obtained by reconstructing the background value, which can improve the modelling accuracy of the traditional model, is proposed in this paper.

The application of trigonometric grey prediction model to average per capita natural gas consumption of households in China

2019 ◽  
Vol 9 (1) ◽  
pp. 19-30 ◽  
Author(s):  
Qiuping Wang ◽  
Subing Liu ◽  
Haixia Yan
Keyword(s):  
Natural Gas ◽  
Prediction Model ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Per Capita

Purpose Due to high efficiency and low carbon of natural gas, the consumption of natural gas is increasing rapidly, and the prediction of natural gas consumption has become the focus. The purpose of this paper is to employ a prediction technique by combining grey prediction model and trigonometric residual modification for predicting average per capita natural gas consumption of households in China. Design/methodology/approach The GM(1,1) model is utilised to obtain the tendency term, then the generalised trigonometric model is used to catch the periodic phenomenon from the residual data of GM(1,1) model for improving predicting accuracy. Findings The case verified the view of Xie and Liu: “When the value of a is less, DGM model and GM(1,1) model can substitute each other.” The combination of the GM(1,1) and the trigonometric residual modification technique can observably improve the predicting accuracy of average per capita natural gas consumption of households in China. The mean absolute percentage errors of GM(1,1) model, DGM(1,1), unbiased grey forecasting model, and TGM model in ex post testing stage (from 2013 to 2015) are 32.5510, 33.5985, 36.9980, and 5.2996 per cent, respectively. The TGM model is suitable for the prediction of average per capita natural gas consumption of households in China. Practical implications According to the historical data of average per capita natural gas consumption of households in China, the authors construct GM(1,1) model, DGM(1,1) model, unbiased grey forecasting model, and GM(1,1) model with trigonometric residual modification. The accuracy of TGM is the best. TGM helps to improve the accuracy of GM(1,1). Originality/value This paper gives a successful practical application of grey model GM(1,1) with the trigonometric residual modification, where the cyclic variations exist in the residual series. The case demonstrates the effectiveness of trigonometric grey prediction model, which is helpful to understand the modeling mechanism of trigonometric grey prediction model.

Application of a novel grey forecasting model with time power term to predict China's GDP

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chong Liu ◽  
Wanli Xie ◽  
Tongfei Lao ◽  
Yu-ting Yao ◽  
Jun Zhang
Keyword(s):  
Economic Development ◽  
Prediction Model ◽  
Future Development ◽  
Development Trend ◽  
Forecasting Model ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model ◽  
Grey Forecasting ◽  
Grey Forecasting Model

PurposeGross domestic product (GDP) is an important indicator to measure a country's economic development. If the future development trend of a country's GDP can be accurately predicted, it will have a positive effect on the formulation and implementation of the country's future economic development policies. In order to explore the future development trend of China's GDP, the purpose of this paper is to establish a new grey forecasting model with time power term to forecast GDP.Design/methodology/approachFirstly, the shortcomings of the traditional grey prediction model with time power term are found out through analysis, and then the generalized grey prediction model with time power term is established (abbreviated as PTGM (1,1, α) model). Secondly, the PTGM (1,1, α) model is improved by linear interpolation method, and the optimized PTGM (1,1, α) model is established (abbreviated as OPTGM (1,1, α) model), and the parameters of the OPTGM (1,1, α) model are solved by the quantum genetic algorithm. Thirdly, the advantage of the OPTGM (1,1, α) model over the traditional grey models is illustrated by two real cases. Finally the OPTGM (1,1, α) model is used to predict China's GDP from 2020 to 2029.FindingsThe OPTGM (1,1, α) model is more suitable for predicting China's GDP than other grey prediction models.Originality/valueA new grey prediction model with time power term is proposed.

scholarly journals A Novel Extrapolation-Based Grey Prediction Model for Forecasting China’s Total Electricity Consumption

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xin-bo Yang
Keyword(s):  
Prediction Model ◽  
Extreme Values ◽  
Model Performance ◽  
Electricity Consumption ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Background Value ◽  
Development Policies ◽  
The Government

Accurately forecasting China’s total electricity consumption is of great significance for the government in formulating sustainable economic development policies, especially, China as the largest total electricity consumption country in the world. The calculation method of the background value of the GM(1, 1) model is an important factor of unstable model performance. In this paper, an extrapolation method with variable weights was used for calculating the background value to eliminate the influence of the extreme values on the performance of the GM(1, 1) model, and the novel extrapolation-based grey prediction model called NEGM(1, 1) was proposed and optimized. The NEGM(1, 1) model was then used to simulate the total electricity consumption in China and found to outperform other grey models. Finally, the total electricity consumption of China from 2018 to 2025 was forecasted. The results show that China’s total electricity consumption will be expected to increase slightly, but the total is still very large. For this, some corresponding recommendations to ensure the effective supply of electricity in China are suggested.

A novel adaptive discrete grey prediction model for forecasting development in energy consumption structure—from the perspective of compositional data

2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wuyong Qian ◽  
Hao Zhang ◽  
Aodi Sui ◽  
Yuhong Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Compositional Data ◽  
Development Trend ◽  
Grey Prediction ◽  
Content Type ◽  
Consumption Structure ◽  
Grey Prediction Model ◽  
Proposed Model

PurposeThe 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/approachDue 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.FindingsChina'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/valueThe 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.

Prediction and optimization of chemical fiber spinning tension based on grey system theory

2018 ◽  
Vol 89 (15) ◽  
pp. 3067-3079 ◽  
Author(s):  
Qihong Zhou ◽  
Tianlun Wei ◽  
Yiping Qiu ◽  
Fangmin Tang ◽  
Lixin Yin ◽  
...  
Keyword(s):  
Prediction Model ◽  
Process Parameters ◽  
Prediction Accuracy ◽  
Fiber Spinning ◽  
Chemical Fiber ◽  
Grey Prediction ◽  
Modeling Error ◽  
Grey Prediction Model ◽  
Background Value ◽  
Winding Tension

Based on the grey prediction model, this paper studied the effect of the chemical fiber spinning process parameters on the winding tension. Suitable process parameters were selected to carry out grey incidence analysis with winding tension, and the feasibility of the grey prediction model in spinning tension prediction was validated by the designed experiments. The corresponding algorithm routines of various grey prediction models were written in MATLAB. The single-variable grey prediction model of GM(1,1) showed a higher prediction accuracy in the effect of the single process parameter changing on spinning tension; when multiple process parameters changed at the same time, the average modeling error of the MGM(1, n) multi-variable grey prediction model was 7.70%, and the maximum error was as high as 32.99%. The original MGM(1, n) model was optimized and the model background value was adjusted by using the auto-optimization and weighting method. The average modeling error of the improved model was reduced to 2.02%, which could meet the general accuracy requirement of tension prediction. Further combining fractional-order accumulation and adjusting the background value coefficient α and the cumulative order r jointly, the smallest prediction error was found among the 100,000 combinations, and the final error was further reduced to 1.30%. The results show that the grey prediction model is suitable and effective for predicting the spinning tension based on the process parameters. Appropriate model improvement mechanisms will increase the prediction accuracy significantly. This application provides a suitable method for spinning tension prediction, which has great significance for the tension control of chemical fiber products.

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

2019 ◽  
Vol 9 (4) ◽  
pp. 464-471 ◽  
Author(s):  
Erkan Kose ◽  
Levent Tasci
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Water Level ◽  
Influential Factor ◽  
Grey Prediction ◽  
Estimation Model ◽  
Content Type ◽  
Grey Prediction Model ◽  
Great Consistency ◽  
Deformation Level

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.

Weapon equipment management cost prediction based on forgetting factor recursive GM (1,1) model

2019 ◽  
Vol 10 (1) ◽  
pp. 38-45
Author(s):  
Subing Liu ◽  
Yin Chunwu ◽  
Cao Dazhi
Keyword(s):  
Prediction Model ◽  
Recursive Estimation ◽  
Model Parameters ◽  
Grey Prediction ◽  
Forgetting Factor ◽  
Content Type ◽  
Grey Prediction Model ◽  
Model Based ◽  
New Information ◽  
Equipment System

Purpose The purpose of this paper is to provide a new recursive GM (1,1) model based on forgetting factor and apply it to the modern weapon and equipment system. Design/methodology/approach In order to distinguish the contribution of new and old data to the grey prediction model with new information, the authors add forgetting factor to the objective function. The purpose of the above is to realize the dynamic weighting of new and old modeling data, and to gradually forget the old information. Second, the recursive estimation algorithm of grey prediction model parameters is given, and the new information is added in real time to improve the prediction accuracy of the model. Findings It is shown that the recursive GM (1,1) model based on forgetting factor can achieve both high effectiveness and high efficiency. Originality/value The paper succeeds in proposing a recursive GM (1,1) model based on forgetting factor, which has high accuracy. The model is applied to the field of modern weapon and equipment system and the result the model is better than the GM(1,1) model. The experimental results show the effectiveness and the efficiency of the prosed method.

Analyzing China’s OFDI using a novel multivariate grey prediction model with Fourier series

2019 ◽  
Vol 12 (3) ◽  
pp. 352-371 ◽  
Author(s):  
Hang Jiang ◽  
Yi-Chung Hu ◽  
Jan-Yan Lin ◽  
Peng Jiang
Keyword(s):  
Fourier Series ◽  
Prediction Model ◽  
Technological Development ◽  
Spillover Effect ◽  
International Investment ◽  
Chinese Government ◽  
Investment Strategies ◽  
Grey Prediction ◽  
Content Type ◽  
Grey Prediction Model

Purpose With the development of economy, China’s OFDI constantly increase in recent year. Meanwhile, OFDI has spillover effect on economic development and technological development of home country. Thus, accurate OFDI prediction is a prerequisite for the effective development of international investment strategies. The purpose of this paper is to predict China’s OFDI accurately using a novel multivariable grey prediction model with Fourier series. Design/methodology/approach This paper applied a multivariable grey prediction model, GM(1,N), to forecast China’s OFDI. In order to improve the prediction accuracy and without changing local characteristics of grey model prediction, this paper proposed a novel grey prediction model to improve the performance of the traditional GM(1,N) model by combining with residual modification model using GM(1,1) model and Fourier series. Findings The coefficients indicate that the export and GDP have positive influence on China’s OFDI, and, according to the prediction result, China’s OFDI shows a growing trend in next five years. Originality/value This paper proposed an effective multivariable grey prediction model that combined the traditional GM(1,N) model with a residual modification model in order to predict China’s OFDI. Accurate forecasting of OFDI provides reference for the Chinese Government to implement international investment strategies.

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