scholarly journals Forecasting China’s per Capita Living Energy Consumption by Employing a Novel DGM (1, 1, tα) Model with Fractional Order Accumulation

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yumu Lu ◽  
Chong Liu ◽  
Haodan Pang ◽  
Ting Feng ◽  
Zijie Dong
Keyword(s):  
Energy Consumption ◽  
Fractional Order ◽  
Prediction Models ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Annual Increment ◽  
Model Based ◽  
Proposed Model ◽  
Per Capita

The living energy consumption of residents has become an important technical index to promote the economic and social development strategy. The country’s medium- and short-term living energy consumption is featured with both a certainty of annual increment and an uncertainty of random variation. Thus, it can be seen as a typical grey system and shall be suitable for the grey prediction model. In order to explore the future development trend of China’s per capita living energy consumption, this paper establishes a novel grey model based on the discrete grey model with time power term and the fractional accumulation (FDGM (1, 1, tα) for short) for forecasting China’s per capita living energy consumption, which makes the existing model to adapt to different time series by adjusting fractional order accumulation parameter and power term. In order to verify the feasibility and effectiveness of the novel model, the proposed and eight other existing grey prediction models are applied to the case of China’s per capita living energy consumption. The results show that the proposed model is more suitable for predicting China’s per capita energy consumption than the other eight grey prediction models. Finally, the proposed model based on metabolism mechanism is used to predict China’s per capita living energy consumption from 2018 to 2029, which can provide a reference for energy companies or government decision makers.

scholarly journals An Improved Nonequidistant Grey Model Based on Simpson Formula and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiming Hu ◽  
Chong Liu
Keyword(s):  
Prediction Accuracy ◽  
Prediction Models ◽  
Prediction Performance ◽  
Grey System Theory ◽  
Grey Model ◽  
Grey Prediction ◽  
Grey System ◽  
Proposed Model ◽  
Grey Models ◽  
High Prediction

Grey prediction models have been widely used in various fields of society due to their high prediction accuracy; accordingly, there exists a vast majority of grey models for equidistant sequences; however, limited research is focusing on nonequidistant sequence. The development of nonequidistant grey prediction models is very slow due to their complex modeling mechanism. In order to further expand the grey system theory, a new nonequidistant grey prediction model is established in this paper. To further improve the prediction accuracy of the NEGM (1, 1, t2) model, the background values of the improved nonequidistant grey model are optimized based on Simpson formula, which is abbreviated as INEGM (1, 1, t2). Meanwhile, to verify the validity of the proposed model, this model is applied in two real-world cases in comparison with three other benchmark models, and the modeling results are evaluated through several commonly used indicators. The results of two cases show that the INEGM (1, 1, t2) model has the best prediction performance among these competitive models.

scholarly journals Forecasting CO2 Emissions Using a Novel Conformable Fractional Order Discrete Grey Model

2021 ◽  
Author(s):  
Peng Zhu ◽  
Wanli Xie ◽  
Yunshen Shi ◽  
Mingyong Pang ◽  
Yuhui Shi
Keyword(s):  
Carbon Dioxide ◽  
Fractional Order ◽  
Carbon Dioxide Emissions ◽  
Industrial Development ◽  
Grey Model ◽  
Optimal Order ◽  
Low Carbon ◽  
Grey System ◽  
Industrial Carbon ◽  
Proposed Model

Abstract Accurate and scientific forecasting of carbon dioxide emissions will help make better industrial carbon emission planning so as to promote low-carbon industrial development and achieve sustainable economic growth. For depressing the disturbance of various elements, grey system-based models play an important role in forecasting science. In this paper, we extend the cumulative order from integer order to fractional order based on the discrete gray model, which we call CFDGM (1,1). After introducing the free quantity of the model order, the accuracy of the prevenient grey-based models can be further enhanced. We selected the data for carbon dioxide production by Germany, Japan, and Thailand for modeling. To obtain the optimal order of our grey model, we selected four optimizers to search for the order. The results show that although the search history of the four types of optimizers is different, the search results are the same, which proves that the four types of optimizers are stable and reliable, and the order for which we searched is reliable. By substituting the optimal order into CFDGM (1,1), we obtained the fitting and prediction error of the proposed model. The final results show that a satisfactory fitting effect and forecasting effect is obtained by our proposed model.

The housing demand analysis and prediction of the real estate based on the AWGM (1, N) model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Xiong ◽  
Huan Guo ◽  
Xi Hu
Keyword(s):  
Real Estate ◽  
Housing Market ◽  
Housing Demand ◽  
Grey Model ◽  
Content Type ◽  
First Order ◽  
Model Based ◽  
Per Capita ◽  
Improved Model ◽  
Grey Relational

PurposeThe purpose of this paper is to seek to drive the modernization of the entire national economy and maintain in the long-term stability of the whole society; this paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem.Design/methodology/approachThis paper proposes an improved model based on the first-order multivariable grey model [GM (1, N) model] for predicting the housing demand and solving the housing demand problem. First, a novel variable SW evaluation algorithm is proposed based on the sensitivity analysis, and then the grey relational analysis (GRA) algorithm is utilized to select influencing factors of the commodity housing market. Finally, the AWGM (1, N) model is established to predict the housing demand.FindingsThis paper selects seven factors to predict the housing demand and find out the order of grey relational ranked from large to small: the completed area of the commodity housing> the per capita housing area> the one-year lending rate> the nonagricultural population > GDP > average price of the commodity housing > per capita disposable income.Practical implicationsThe model constructed in the paper can be effectively applied to the analysis and prediction of Chinese real estate market scientifically and reasonably.Originality/valueThe factors of the commodity housing market in Wuhan are considered as an example to analyze the sales area of the commodity housing from 2015 to 2017 and predict its trend from 2018 to 2019. The comparison between demand for the commodity housing actual value and one for model predicted value is capability to verify the effectiveness of the authors’ proposed algorithm.

scholarly journals Forecasting Japan’s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model

2019 ◽  
Vol 11 (21) ◽  
pp. 5921 ◽  
Author(s):  
Peng Zhang ◽  
Xin Ma ◽  
Kun She
Keyword(s):  
Energy Consumption ◽  
Solar Energy ◽  
Nonlinear Coefficient ◽  
Prediction Method ◽  
Decision Makers ◽  
Grey Model ◽  
Correct Prediction ◽  
Grey Prediction ◽  
Real World Datasets ◽  
Application Fields

Energy consumption is an essential basis for formulating energy policy and programming, especially in the transition of energy consumption structure in a country. Correct prediction of energy consumption can provide effective reference data for decision-makers and planners to achieve sustainable energy development. Grey prediction method is one of the most effective approaches to handle the problem with a small amount of historical data. However, there is still room to improve the prediction performance and enlarge the application fields of the traditional grey model. Nonlinear grey action quantity can effectively improve the performance of the grey prediction model. Therefore, this paper proposes a novel incomplete gamma grey model (IGGM) with a nonlinear grey input over time. The grey input of the IGGM model is a revised incomplete gamma function of time in which the nonlinear coefficient determines the performance of the IGGM model. The WOA algorithm is employed to seek for the optimal incomplete coefficient of the IGGM model. Then, the validations of IGGM are performed on four real-world datasets, and the results exhibit that the IGGM model has more advantages than the other state-of-the-art grey models. Finally, the IGGM model is applied to forecast Japan’s solar energy consumption in the next three years.

scholarly journals Application of a Fractional Grey Prediction Model Based on a Filtering Algorithm in Image Processing

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyu Tong ◽  
Kailiang Shao ◽  
Xilin Luo ◽  
Huiming Duan
Keyword(s):  
Image Processing ◽  
Prediction Model ◽  
Grey System Theory ◽  
Grey Prediction ◽  
Grey System ◽  
Middle Point ◽  
Filtering Algorithm ◽  
Grey Correlation ◽  
Grey Prediction Model ◽  
Model Based

Image filtering can change or enhance an image by emphasizing or removing certain features of the image. An image is a system in which some information is known and some information is unknown. Grey system theory is an important method for dealing with this kind of system, and grey correlation analysis and grey prediction modeling are important components of this method. In this paper, a fractional grey prediction model based on a filtering algorithm by combining a grey correlation model and a fractional prediction model is proposed. In this model, first, noise points are identified by comparing the grey correlation and the threshold value of each pixel in the filter window, and then, through the resolution coefficient of the important factor in image processing, a variety of grey correlation methods are compared. Second, the image noise points are used as the original sequence by the filter pane. The grey level of the middle point is predicted by the values of the surrounding pixel points combined with the fractional prediction model, replacing the original noise value to effectively eliminate the noise. Finally, an empirical analysis shows that the PSNR and MSE of the new model are approximately 27 and 140, respectively; these values are better than those of the comparison models and achieve good processing effects.

Bankruptcy prediction using multivariate grey prediction models

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yi-Chung Hu ◽  
Peng Jiang ◽  
Hang Jiang ◽  
Jung-Fa Tsai
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Bankruptcy Prediction ◽  
Financial Data ◽  
Grey Prediction ◽  
Grey System ◽  
Content Type ◽  
Business Environments ◽  
The Face ◽  
Financial Decision

PurposeIn the face of complex and challenging economic and business environments, developing and implementing approaches to predict bankruptcy has become important for firms. Bankruptcy prediction can be regarded as a grey system problem because while factors such as the liquidity, solvency and profitability of a firm influence whether it goes bankrupt, the precise manner in which these factors influence the discrimination between failed and non-failed firms is uncertain. In view of the applicability of multivariate grey prediction models (MGPMs), this paper aimed to develop a grey bankruptcy prediction model (GBPM) based on the GM (1, N) (BP-GM (1, N)).Design/methodology/approachAs the traditional GM (1, N) is designed for time series forecasting, it is better to find an appropriate permutation of firms in the financial data as if the resulting sequences are time series. To solve this challenging problem, this paper proposes GBPMs by integrating genetic algorithms (GAs) into the GM (1, N).FindingsExperimental results obtained for the financial data of Taiwanese firms in the information technology industries demonstrated that the proposed BP-GM (1, N) performs well.Practical implicationsAmong artificial intelligence (AI)-based techniques, GBPMs are capable of explaining which of the financial ratios has a stronger impact on bankruptcy prediction by driving coefficients.Originality/valueApplying MGPMs to a problem without relation to time series is challenging. This paper focused on bankruptcy prediction, a crucial issue in financial decision-making for businesses, and proposed several GBPMs.

Grey system based novel approach for stock market forecasting

2015 ◽  
Vol 5 (2) ◽  
pp. 178-193 ◽  
Author(s):  
R.M. Kapila Tharanga Rathnayaka ◽  
D.M.K.N Seneviratna ◽  
Wei Jianguo
Keyword(s):  
Time Series ◽  
Prediction Models ◽  
Stock Exchange ◽  
Moving Average ◽  
Grey Prediction ◽  
Grey System ◽  
Short Term ◽  
Content Type ◽  
Stock Market Forecasting ◽  
Novel Approach

Purpose – Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions. Design/methodology/approach – High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose. Findings – The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings. Practical implications – Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches. Originality/value – However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.

scholarly journals A Novel Grey Power-markov Model for the Prediction of China's Electricity Consumption

2021 ◽  
Author(s):  
Liqin Sun ◽  
Youlong Yang ◽  
Tong Ning ◽  
Jiadi Zhu
Keyword(s):  
Prediction Models ◽  
Demand Forecasting ◽  
Electricity Consumption ◽  
Original Data ◽  
Development Trend ◽  
Statistical Hypothesis ◽  
Grey Prediction ◽  
New Information ◽  
Proposed Model ◽  
Grey Models

Abstract The grey prediction models of time series are widely used in demand forecasting because only limited data can be used to build the models and no statistical hypothesis is needed. In this paper, a grey power Markov prediction model (RGPMM(λ,1,1)) with time-varying parameters is proposed. This model is based on the principle of “new information priority”, combined with rolling mechanism and Markov theory, and the prediction residual error is modified to further improve the prediction accuracy. Compared with the classic grey models, the new model not only overcomes the inherent defect of poor adaptability to the original data, but also uses real-time information to better reflect the nonlinear characteristics of the original data, so it can be used to describe and predict the nonlinear development trend of things. In order to verify the validity and applicability of the model, the proposed model is used to forecast the total electric consumption in China. The experimental results show that the proposed model has a better prediction effect than other grey models. The proposed model is used to forecast China’s total electricity consumption in the next six years from 2018 to 2023.

scholarly journals The Optimized Multivariate Grey Prediction Model Based on Dynamic Background Value and Its Application

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tongfei Lao ◽  
Xiaoting Chen ◽  
Jianian Zhu
Keyword(s):  
Time Series ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Small Sample ◽  
Grey Prediction ◽  
Dynamic Background ◽  
Background Values ◽  
Grey Prediction Model ◽  
Model Based

As a tool for analyzing time series, grey prediction models have been widely used in various fields of society due to their higher prediction accuracy and the advantages of small sample modeling. The basic GM (1, N) model is the most popular and important grey model, in which the first “1” stands for the “first order” and the second “N” represents the “multivariate.” The construction of the background values is not only an important step in grey modeling but also the key factor that affects the prediction accuracy of the grey prediction models. In order to further improve the prediction accuracy of the multivariate grey prediction models, this paper establishes a novel multivariate grey prediction model based on dynamic background values (abbreviated as DBGM (1, N) model) and uses the whale optimization algorithm to solve the optimal parameters of the model. The DBGM (1, N) model can adapt to different time series by changing parameters to achieve the purpose of improving prediction accuracy. It is a grey prediction model with extremely strong adaptability. Finally, four cases are used to verify the feasibility and effectiveness of the model. The results show that the proposed model significantly outperforms the other 2 multivariate grey prediction models.

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