Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation

Wang, Zheng‐Xin; Li, Dandan; Zheng, Honghao

doi:10.1016/j.physa.2019.123341

Cited by 61 publications

(33 citation statements)

References 29 publications

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“…From the above three types of models, it can be seen that the infectious disease model and time series model may need a large amount of data for accurate parameter identification. Literatures [12] , [13] , [14] point that the machine learning model may need a large amount of data for training and testing to achieve accurate results. In the research on the development trend of infectious diseases, Richards model [15] has been widely used in a variety of infectious diseases due to its advantages in processing saturated S-shaped data.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, in systems that deal with incomplete information, the grey model stands out by virtue of its "simple model, strong adaptability, and easy parameter changes". It is widely used in energy, finance, transportation, environment, manufacturing, materials and other industries [ [12] , [13] , [14] , [20] , [21] , [22] , [23] , [24] , [25] ]. In the field of infectious diseases, the grey model is also widely used.…”

Section: Introductionmentioning

confidence: 99%

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A novel grey model based on traditional Richards model and its application in COVID-19

Luo

Duan

2021

Chaos, Solitons & Fractals

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Highlights A novel grey Richards model GERM(1,1, ) is proposed. The optimal nonlinear terms and background value of the novel model are determined by Genetic algorithm. The comparative study shows that the new model is superior to the other seven benchmark models. The predict the daily number of new confirmed cases of COVID-19 of four regions are projected.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel grey model based on traditional Richards model and its application in COVID-19

Luo

Duan

2021

Chaos, Solitons & Fractals

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“…Obviously, Professor Deng [18], a pioneer on grey system theory, would like to solve this topic and gave an innovative theory often called the grey system theory. In particular, the grey forecasting model, a key branch of this theory, has been widely concerned and applied in many fields, including engineering, economy, and especially energy (see [19][20][21][22][23][24][25][26][27][28]). In addition, as a basic model in grey system model, which is abbreviated as GM (1,1).…”

Section: Research On the Grey System Modelmentioning

confidence: 99%

Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Zheng

Jiang

et al. 2020

Complexity

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As is known, natural gas consumption has been acted as an extremely important role in energy market of China, and this paper is to present a novel grey model which is based on the optimized nonhomogeneous grey model (ONGM (1,1)) in order to accurately predict natural gas consumption. This study begins with proving that prediction results are independent of the first entry of original series using the product theory of determinant; on this basis, it is a reliable approach by inserting an arbitrary number in front of the first entry of original series to extract messages, which has been proved that it is an appreciable approach to increase prediction accuracy of the traditional grey model in the earlier literature. An empirical example often appeared in testing for prediction accuracy of the grey model is utilized to demonstrate the effectiveness of the proposed model; the numerical results indicate that the proposed model has a better prediction performance than other commonly used grey models. Finally, the proposed model is applied to predict China’s natural gas consumption from 2019 to 2023 in order to provide some valuable information for energy sectors and related enterprises.

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“…2. Using the least squares estimation to obtain the parameter vector â according to (3) to (8), the obtained time response equation X (0) and the subtractive reduction equation x (0) (k) , then establish a simulation sequence.If the requirement for simulation accuracy is low, a preliminary simulation can be performed at this time, but the simulation value gradually increases with the prediction time. 3.…”

Section: Residual Gm Network Model Based On Bp-pemea Correctionmentioning

confidence: 99%

Research on residual GM optimization based on PEMEA-BP correction

Duan

Zhu

Wei

et al. 2020

Sci Rep

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With the advantages of small samples and high accuracy, Grey Model (GM) still has two major problems need to be addressed, high input data requirements and large margin of error. Hence, this paper proposes an algorithm based on Populational Entropy Based Mind Evolutionary Algorithm-Error Back Propagation Training Artificial Neural Algorithm to modify GM residual tail, which will not only keep the advantages of GM, but also expand its scope of use to various non-linear and even multidimensional objects. Meanwhile, it can avoid defects of other algorithms, such as slow convergence and easy to fall into the local minimum. In small samples data experiments, judging from SSE, MAE, MSE, MAPE, MRE and other indicators, this new algorithm has significant advantage over GM, BP algorithm and combined genetic algorithm in terms of simulation accuracy and convergence speed.

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Model comparison of GM(1,1) and DGM(1,1) based on Monte-Carlo simulation

Cited by 61 publications

References 29 publications

A novel grey model based on traditional Richards model and its application in COVID-19

A novel grey model based on traditional Richards model and its application in COVID-19

Forecasting Natural Gas Consumption of China Using a Novel Grey Model

Research on residual GM optimization based on PEMEA-BP correction

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