Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

A self-adaptive grey forecasting model and its application

GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.

The Further Development and Application of Grey Forecasting Model

In this paper, the method which forecasts original sequences {x (0)(k)} with logarithmic function or with power function has been complemented, and the method which handles original sequences by logarithmic function-power function transformation or by power function-logarithmic function transformation has been presented, then smooth degree and precision of forecasting of discrete data have been improved.

A novel fractional grey forecasting model with variable weighted buffer operator and its application in forecasting China's crude oil consumption

Oil is an important strategic material and civil energy.Accurate prediction of oil consumption can provide basis for relevant departments to reasonably arrange crude oil production,oil import and export,and optimize the allocation of social resources.Therefore,a new grey model FENBGM(1,1)is proposed to predict oil consumption in China.Firstly,the grey effect of the traditional GM(1,1)model was transformed into a quadratic equation.Four different parameters were introduced to improve the accuracy of the model,and the new initial conditions were designed by optimizing the initial values by weighted buffer operator.Combined with the reprocessing of the original data,the scheme eliminates the random disturbance effect,improves the stability of the system sequence,and can effectively extract the potential pattern of future development.Secondly,the cumulative order of the new model was optimized by fractional cumulative generation operation.At the same time,the smoothness rate quasi-smoothness condition was introduced to verify the stability of the model,and the particle swarm optimization algorithm(PSO)was used to search the optimal parameters of the model to enhance the adaptability of the model.Based on the above improvements,the new combination prediction model overcomes the limitation of the traditional grey model and obtains more accurate and robust prediction results.Then,taking the petroleum consumption of China's manufacturing industry and transportation,storage and postal industry as an example,this paper verifies the validity of FENBGM(1,1)model,analyzes and forecasts China's crude oil consumption with several commonly used forecasting models,and uses FENBGM(1,1)model to forecast China's oil consumption in the next four years.The results show that FENBGM(1,1)model performs best in all cases.Finally,based on the prediction results of FENBGM(1,1)model,some reasonable suggestions are put forward for China's oil consumption planning.

Application of Improved Grey Prediction Model to Petroleum Cost Forecasting

The grey theory is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. In this paper, an improved grey model using step function was proposed. Petroleum cost forecast of the Henan oil field was used as the case study to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed method obviously could improve the prediction accuracy of the original grey model.

Improved grey prediction model based on exponential grey action quantity

With the passage of time, it has become important to investigate new methods for updating data to better fit the trends of the grey prediction model. The traditional GM（1,1） usually sets the grey action quantity as a constant. Therefore, it cannot effectively fit the dynamic characteristics of the sequence, which results in the grey model having a low precision. The linear grey action quantity model cannot represent the index change law. This paper presents a grey action quantity model, the exponential optimization grey model（EOGM（1,1））, based on the exponential type of grey action quantity; it is constructed based on the exponential characteristics of the grey prediction model. The model can fully reflect the exponential characteristics of the simulation series with time. The exponential sequence has a higher fitting accuracy. The optimized result is verified using a numerical example for the fluctuating sequence and a case study for the index of the tertiary industry＇s GDP. The results show that the model improves the precision of the grey forecasting model and reduces the prediction error.

Expansion modelling of discrete grey model based on multi-factor information aggregation

This paper aims to study a novel expansion discrete grey forecasting model, which could aggregate input information more effectively. In general, existing multi-factor grey forecasting models, such as one order and h variables grey forecasting model （GM （1, h））, always aggregate the main system variable and independent variables in a linear form rather than a nonlinear form, while a nonlinear form could be used in more cases than the linear form. And the nonlinear form could aggregate collinear independent factors, which widely lie in many multi-factor forecasting problems. To overcome this problem, a new approach, named as the Solow residual method, is proposed to aggregate independent factors. And a new expansion model, feedback multi-factor discrete grey forecasting model based on the Solow residual method （abbreviated as FDGM （1, h））, is proposed accordingly. Then the feedback control equation and the parameters＇ solution of the FDGM （1, h） model are given. Finally, a real application is used to test the modelling accuracy of the FDGM （1, h） model. Results show that the FDGM （1, h） model is much better than the nonhomogeneous discrete grey forecasting model （NDGM） and the GM （1, h） model.

The Modified GM( 1 , 1) Grey Forecast Model

Because the impacts of the factors such as some disturbances are graduallyadded into the system, the grey forecast results will deviate from the systemtrue value. To improve the forecast precision, Pro-Dens Julons provided twomethfor-But they had not consider the impact of artificial disturbance. LiZhihua et al. of Qinghua Univ. presented another method. This paper revisesthe method and make it be a spocial case.

Research on Ratio of New Energy Vehicles to Charging Piles in China

With the widespread of new energy vehicles, charging piles have alsobeen continuously installed and constructed. In order to make the number of pilesmeet the needs of the development of new energy vehicles, this study aims toapply the method of system dynamics and combined with the grey prediction theory to determine the parameters as well as to simulate and analyze the ratio ofvehicles to chargers. Through scenario analysis, it is predicted that by 2030, thisratio will gradually decrease from 1.79 to 1. In order to achieve this ratio as 1:1, itis necessary to speed up the construction of public charging station or privatecharging station. Due to global warming, the attitudes of countries towards fuelvehicles have become increasingly tough. There is huge uncertainty in the growthrate of electric vehicles. Therefore, it is recommended that the construction ofcharging station be deployed in advance to avoid hindering the development ofelectric vehicles in the future.

Coupling coordination and evolution of finance,economy and ecological environment:The case of Silk Road provinces

Based on the comparative analysis of the coupling mechanism between finance,economy and ecological environment,this paper uses the coupling coordination degree and grey prediction model to measure the comprehensive evaluation value and coupling coordination degree of the financial,economic and ecological environment composite system of five provinces along the Silk Road in China from 2010 to 2019,and analyzes the evolution law of the coupling and coordinated development of finance,economy and ecological environment from the perspective of system coordinated development.The results show that:(1)The development of the financial and economic systems of the provinces along the Silk Road shows a relatively continuous and stable good trend,while the development of the ecosystem shows more obvious fluctuations.(2)In general,the overall level of the comprehensive evaluation of each system in the provinces along the route has shown a trend of improvement,but the level is not high.(3)During the investigation by provinces,it is found that the coordination level of the coupling coordination degree of finance,economy and ecological environment of each province is in three stages:moderate disorder,mild disorder,and imminent imbalance.However,Shaanxi Province has changed from imminent imbalance to barely coordination earlier,which indicates that there is regional heterogeneity in the three coupling coordination relationships among regions.(4)According to the calculation of the gray GM(1,1)model,the coupling coordination degree in Shaanxi Province will maintain a good upward trend and reach a intermediate coordination stage in 2023,while the other provinces will enter the barely coordination and primary coordination stages respectively with a slightly slower growth rate.Finally,based on the research conclusions,this paper puts forward some effective policy recommendations for the coordinated development of regional finance,economy and ecological environment,such as dredging the diffusion channels of regional financial resources,guiding market funds to participate in ecological project construction,and advocating the development of circular economy.

Study of evolution forecast of the area of the Huanghe River mouth

From 1979 to 1989, the current Qingshuigou course of the Huanghe River formed a sub - delta which resembles a beak extending into the Laizhou Bay. It covers 618 km2 in area. To meet the needs of developing and constructing the Huanghe River Delta and under the presupposition of keeping the current course for 15-20 a, we forecast mainly by using the OM (1, 1) model that the front border of the sub-delta will be close to 119°30'E and its area will become 923 km2by the end of the year 2000. The Huanghe River will make land 760 km2 in area.

Grey System Forecast for Firing Accuracy of Gun

In this paper, the system and subsystem forecast models for firing accuracy have been built by means of theory of Grey System Forecast. It has provided a scientific forecasting method for micro-error-control and macro-error-control and improving the firing accuracy.

Forecasting of dissolved oxygen in the Guanting reservoir using an optimized NGBM(1,1) model

An optimized nonlinear grey Bernoulli model was proposed by using a particle swarm optimization algorithm to solve the parameter optimization problem. In addition, each item in the first-order accumulated generating sequence was set in turn as an initial condition to determine which alternative would yield the highest forecasting accuracy. To test the forecasting performance, the optimized models with different initial conditions were then used to simulate dissolved oxygen concentrations in the Guantlng reservoir inlet and outlet （China）. The empirical results show that the optimized model can remarkably improve forecasting accuracy, and the particle swarm optimization technique is a good tool to solve parameter optimization problems. What＇s more, the optimized model with an initial condition that performs well in in-sample simulation may not do as well as in out-of-sample forecasting.