scholarly journals Calculation and realization of new method grey residual error correction model

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254154
Author(s):  
Lifang Xiao ◽  
Xiangyang Chen ◽  
Hao Wang
Keyword(s):  
Prediction Model ◽  
Least Square Method ◽  
Residual Error ◽  
Least Square ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
True Value ◽  
Prediction Function ◽  
Fitting Method

Aiming at the problem of prediction accuracy of stochastic volatility series, this paper proposes a method to optimize the grey model(GM(1,1)) from the perspective of residual error. In this study, a new fitting method is firstly used, which combines the wavelet function basis and the least square method to fit the residual data of the true value and the predicted value of the grey model(GM(1,1)). The residual prediction function is constructed by using the fitting method. Then, the prediction function of the grey model(GM(1,1)) is modified by the residual prediction function. Finally, an example of the wavelet residual-corrected grey prediction model (WGM) is obtained. The test results show that the fitting accuracy of the wavelet residual-corrected grey prediction model has irreplaceable advantages.

scholarly journals A Nonhomogeneous Multivariable Grey Prediction NMGM Modeling Mechanism and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Haixia Wang ◽  
Lingdi Zhao
Keyword(s):  
Prediction Model ◽  
Least Square Method ◽  
Original Data ◽  
Least Square ◽  
The Novel ◽  
Grey Prediction ◽  
Prediction Theory ◽  
Grey Prediction Model ◽  
Index Data ◽  
Proposed Model

The purpose of this paper is to explore modeling mechanism of a nonhomogeneous multivariable grey prediction NMGM(1, m, kα) model and its application. Although multi-variable grey prediction MGM(1, m) model has been employed in many fields, its prediction results are not always satisfactory. Traditional MGM(1, m) model is constructed on the hypothesis that original data sequences are in accord with homogeneous index trend; however, the nonhomogeneous index data sequences are the most common data existing in all systems, and how to handle multivariable nonhomogeneous index data sequences is an urgent problem. This paper proposes a novel nonhomogeneous multivariable grey prediction model termed NMGM(1, m, kα) to deal with those data sequences that are not in accord with homogeneous index trend. Based on grey prediction theory, by least square method and solutions of differential equations, the modeling mechanism and time response function of the proposed model are expounded. A case study demonstrates that the novel model provides preferable prediction performance compared with traditional MGM(1, m) model. This work is an extension of the multivariable grey prediction model and enriches the study of grey prediction theory.

scholarly journals Forecasting Chinese Wind Power Installed Capacity Using a Novel Grey Model with Parameters Combination Optimization

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Xiaoshuang Luo ◽  
Bo Zeng ◽  
Hui Li ◽  
Wenhao Zhou
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Models ◽  
Grey Model ◽  
The Novel ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Combination Optimization ◽  
Installed Capacity ◽  
Novel Model

The intermittent and uncertain characteristics of wind generation have brought new challenges for the hosting capacity and the integration of large-scale wind power into the power system. Consequently, reasonable forecasting wind power installed capacity (WPIC) is the most effective and applicable solution to meet this challenge. However, the single parameter optimization of the conventional grey model has some limitations in improving its modeling ability. To this end, a novel grey prediction model with parameters combination optimization is proposed in this paper. Firstly, considering the modeling mechanism and process, the order of accumulation generation of the grey prediction model is optimized by Particle Swarm Optimization (PSO) Algorithm. Secondly, as different orders of accumulation generation correspond to different parameter matrixes, the background value coefficient of the grey prediction model is optimized based on the optimal accumulation order. Finally, the novel model of combinational optimization is employed to simulate and forecast Chinese WPIC, and the comprehensive error of the novel model is only 1.34%, which is superior to the other three grey prediction models (2.82%, 1.68%, and 2.60%, respectively). The forecast shows that China’s WPIC will keep growing in the next five years, and some reasonable suggestions are put forward from the standpoint of the practitioners and governments.

Application of Improved Grey Prediction Model in Jilin Province of the Software Industry

2014 ◽  
Vol 998-999 ◽  
pp. 1079-1082 ◽  
Author(s):  
Wei Shi Yin ◽  
Pin Chao Meng ◽  
Yan Zhong Li
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Historical Data ◽  
Software Industry ◽  
Experimental Results ◽  
Jilin Province ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey Prediction Model ◽  
Better Than

Based on the modified grey prediction model, the outputs of software industry in Jilin Province were predicted. First the historical data and updated the data were pre-treated by iteration. Then it was found that the results from the modified grey prediction model were better than that from traditional grey prediction model by residual analysis. Finally, the prediction about the outputs of software industry in Jilin Province was given for the next five years. According to the experimental results, our proposed new method obviously can improve the prediction accuracy of the original grey model.

scholarly journals Nonlinear Grey Prediction Model with Convolution Integral NGMC(1,n)and Its Application to the Forecasting of China’s Industrial SO2Emissions

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zheng-Xin Wang
Keyword(s):  
Prediction Model ◽  
Least Squares Method ◽  
Influencing Factor ◽  
Grey Model ◽  
Grey Prediction ◽  
Convolution Integral ◽  
Grey Prediction Model ◽  
Economic Output ◽  
Decision Variables ◽  
Improved Accuracy

The grey prediction model with convolution integral GMC (1,n) is a multiple grey model with exact solutions. To further improve prediction accuracy and describe better the relationship between cause and effect, we introduce nonlinear parameters into GMC (1,n) model and additionally apply a convolution integral to produce an improved forecasting model here designated as NGMC (1,n). The model solving process applied the least-squares method to evaluate the structure parameters of the model: convolution was used to obtain an exact solution with this improved grey model. The nonlinear optimisation took the parameters as the decision variables with the objective of minimising forecasting errors. The GMC (1, 2) and NGMC (1, 2) models were used to predict China’s industrial SO2emissions from the basis of the economic output level as the influencing factor. Results indicated that NGMC (1, 2) can effectively describe the nonlinear relationship between China’s economic output and SO2emissions with an improved accuracy over current GMC (1, 2) models.

scholarly journals Predicting Building Energy Consumption with a New Grey Model

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yan Zhang ◽  
Huiping Wang ◽  
Yi Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Building Energy ◽  
Grey Model ◽  
Annual Growth Rate ◽  
Grey Prediction ◽  
Building Energy Consumption ◽  
Total Energy Consumption ◽  
Grey Prediction Model ◽  
Grey Models

Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, t α )), introduces the modeling mechanism and characteristics of the FDGM (1, 1, t α ), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, t α ) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, t α ) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively.

Dynamic Analysis and Prediction of Ecological Footprint in Jilin Province of China Based on Grey Prediction Model

2014 ◽  
Vol 472 ◽  
pp. 899-903 ◽  
Author(s):  
Biao Gao ◽  
Qing Tao Xu
Keyword(s):  
Prediction Model ◽  
Ecological Footprint ◽  
Jilin Province ◽  
Grey Prediction ◽  
Dynamic Changes ◽  
Ecological Capacity ◽  
Grey Prediction Model ◽  
Ecological Deficit ◽  
Gray Prediction ◽  
Per Capita

The paper calculates ecological footprint per capita and ecological capacity per capita in the Jilin province during 1998 and 2010 by using the ecological footprint theory, and analyzes the dynamic changes of ecological footprint per capita and ecological capacity per capita, and obtains development prediction model of ecological footprint per capita and ecological capacity per capita based on grey prediction model. The results indicate the ecological footprint per capita had increased continuously from 1.7841 hm2 per capita to 3.2013 hm2 per capita between 1998 and 2010. During this period, ecological capacity per capita dropped from 1.3535 hm2 per capita to 1.3028 hm2 per capita. Ecological deficit had increased from 0.4306 hm2 per capita to 1.8985 hm2 per capita that showed that the development of Jilin province was in an unsustainable status. The gray prediction model shows the ecological footprint per capita in the Jilin province will increase from 3.4833 hm2 per capita to 5.7022 hm2 per capita between 2011 and 2020, ecological capacity per capita will drop from 1.2978 hm2 per capita to 1.2676 hm2 per capita and ecological deficit will increase from 2.1855 hm2 per capita to 4.4346 hm2 per capita.

scholarly journals The Parameter Estimation of Double Exponential Pulse Based on a Least Infinity Norm Fitting Method

2021 ◽  
Vol 2083 (4) ◽  
pp. 042002
Author(s):  
Yuewu Shi ◽  
Wei Wang ◽  
Zhizhen Zhu ◽  
Xin Nie
Keyword(s):  
Estimation Error ◽  
Estimation Method ◽  
Maximum Amplitude ◽  
Least Square Method ◽  
Least Square ◽  
Physical Parameters ◽  
Infinity Norm ◽  
Fitting Method ◽  
Double Exponential ◽  
Exponential Pulse

Abstract This paper presents an estimation method of double exponential pulse (DEP) between the physical parameters rise time (t r), full width at half maximum amplitude (t FWHM) and the mathematical parameters α, β. A newly fitting method based on the least infinity norm criterion is proposed to deal with the estimation problem of DEP. The calculation process and equation of parameters of this method is proposed based on an m-th-order polynomial fitting model. This estimation method is compared with the least square method by the same data and fitting function. The results show that the maximum estimation error of parameters of double exponential pulse obtained by the least infinity norm method is 1.5 %.

scholarly journals Forecasting the Number of New Coronavirus Infections Us-ing an Improved Grey Prediction Model

2021 ◽  
Author(s):  
Hui Li ◽  
Bo Zeng ◽  
Jianzhou Wang ◽  
Hua’an Wu
Keyword(s):  
Prediction Model ◽  
Particle Swarm Optimization Algorithm ◽  
Percentage Error ◽  
Small Data ◽  
Grey Prediction ◽  
Matlab Simulation ◽  
Short Term ◽  
New Model ◽  
Grey Prediction Model ◽  
Coronavirus Infections

Background: Recently, a new coronavirus has been rapidly spreading from Wuhan, China. Forecasting the number of infections scientifically and effectively is of great significance to the allocation of medical resources and the improvement of rescue efficiency. Methods: The number of new coronavirus infections was characterized by “small data, poor information” in the short term. The grey prediction model provides an effective method to study the prediction problem of “small data, poor information”. Based on the order optimization of NHGM(1,1,k), this paper uses particle swarm optimization algorithm to optimize the background value, and obtains a new improved grey prediction model called GM(1,1|r,c,u). Results: Through MATLAB simulation, the comprehensive percentage error of GM(1,1|r,c,u), NHGM(1,1,k), UGM(1,1), DGM(1,1) are 2.4440%, 11.7372%, 11.6882% and 59.9265% respectively, so the new model has the best prediction performance. The new coronavirus infections was predicted by the new model. Conclusion: The number of new coronavirus infections in China increased continuously in the next two weeks, and the final infections was nearly 100 thousand. Based on the prediction results, this paper puts for-ward specific suggestions.

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