Discrete grey forecasting model and its optimization

Xie, Naiming; Liu, Sifeng

doi:10.1016/j.apm.2008.01.011

Cited by 430 publications

(213 citation statements)

References 6 publications

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“…From Table 6, it is clear to see that the proposed GM-S-SIGM-GA model receives the highest forecasting accuracy then other compared models, such as GM(1,1) [42], SIGM [1the DGM (1,1) [43], and EDGM [44] models. Firstly, the GM-SIGM-GA model is not always superior to GM(1,1) and SIGM, even when it has employed GA to intelligently determine the most suitable combined weight coefficients.…”

Section: Discussionmentioning

confidence: 99%

“…The test results are illustrated in Table 7, showing that the proposed GM-S-SIGM-GA model has statistical significance compared to other models. GM-S-SIGM-GA vs. GM-SIGM-GA 2 a 2 a GM-S-SIGM-GA vs. GM (1,1) [42] 0 a 0 a GM-S-SIGM-GA vs. SIGM [1] 2 a 2 a GM-S-SIGM-GA vs. DGM [43] 0 a 0 a GM-S-SIGM-GA vs. EDGM [44] 0 a 0 a a Denotes that the GM-S-SIGM-GA model significantly outperforms the other alternative compared models.…”

Section: Improvement Analysismentioning

confidence: 99%

“…Figure 7a demonstrates the additional comparisons in terms of the simulation curves and the forecasting curves, with two other grey-based models, including the (1,1), EDGM, GM-SIGM-GA, GM(1,1), SIGM, GM-SIGM-GA, and GM-S-SIGM-GA models. To further compare the forecasting results of the proposed GM-S-SIGM-GA model with other grey-based models, two grey-based models, namely, the discrete grey model (briefed as DGM(1,1) [43]) and the event difference grey model (briefed as EDGM [44]), are employed. Table 6 summarizes the forecasting results of the GM-S-SIGM-GA, GM-SIGM-GA, GM(1,1), SIGM, DGM(1,1), and EDGM models.…”

Section: Improvement Analysismentioning

confidence: 99%

See 2 more Smart Citations

Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Fan¹,

Wang²,

Hong

2018

Energies

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Along with the high growth rate of economy and fast increasing air pollution, clean energy, such as the natural gas, has played an important role in preventing the environment from discharge of greenhouse gases and harmful substances in China. It is very important to accurately forecast the demand of natural gas in China is for the government to formulate energy policies. This paper firstly proposes a combined forecasting model, name GM-S-SIGM-GA model, to forecast the demand of natural gas in China from 2011 to 2017, by constructing the grey model (GM(1,1)) and the self-adapting intelligent grey model (SIGM), respectively; then, it employs a genetic algorithm to determine the combined weight coefficients between these two models. Finally, using the tendency index (the annual changes of the share of natural gas consumption from the total energy consumption), which completely reveal the annual natural gas consumption share among the market, to successfully adjust the fluctuated changes for each data period. The natural gas demand data from 2002 to 2010 in China are used to model the proposed GM-S-SIGM-GA model, and the data from 2011 to 2017 are used to evaluate the forecasting accuracy. The experimental results demonstrate that the proposed GM-S-SIGM-GA model is superior to other single forecasting models in terms of the mean absolute percentage error (MAPE; 4.48%), the root mean square error (RMSE; 11.59), and the mean absolute error (MAE; 8.41), respectively, and the forecasting performances also receive the statistical significance under 97.5% and 95% confident levels, respectively.

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Section: Discussionmentioning

confidence: 99%

Section: Improvement Analysismentioning

confidence: 99%

Section: Improvement Analysismentioning

confidence: 99%

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Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Fan¹,

Wang²,

Hong

2018

Energies

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“…DGM (1,1), the discrete grey model with a first-order differential equation and one variable, has been shown to be equivalent to the GM(1,1) model under given conditions and to be simpler to use [37]. Xie and Liu discussed in detail the basic principles of DGM(1,1) [38], which has been widely used recently [32,[39][40][41]. Here, we give a concise basic process of DGM(1,1).…”

Section: Fundamental Theoriesmentioning

confidence: 99%

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

Yang

Xiao

Mao

et al. 2016

Entropy

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This paper studies the grey coupled prediction problem of traffic data with panel data characteristics. Traffic flow data collected continuously at the same site typically has panel data characteristics. The longitudinal data (daily flow) is time-series data, which show an obvious intra-day trend and can be predicted using the autoregressive integrated moving average (ARIMA) model. The cross-sectional data is composed of observations at the same time intervals on different days and shows weekly seasonality and limited data characteristics; this data can be predicted using the rolling seasonal grey model (RSDGM (1,1)). The length of the rolling sequence is determined using matrix perturbation analysis. Then, a coupled model is established based on the ARIMA and RSDGM(1,1) models; the coupled prediction is achieved at the intersection of the time-series data and cross-sectional data, and the weights are determined using grey relational analysis. Finally, numerical experiments on 16 groups of cross-sectional data show that the RSDGM(1,1) model has good adaptability and stability and can effectively predict changes in traffic flow. The performance of the coupled model is also better than that of the benchmark model, the coupled model with equal weights and the Bayesian combination model.

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“…For instance, Dan et al [11] proposed a method for grey model improvement using the last item of X (1) as initial value of the grey differential equation to enhance prediction accuracy. Xie and Liu [12] discussed the influence of different fitting points and proposed optimization model fitting point contributes to the model. Another study by Wang et al, [13] introduced a new approach for grey model improvement based on a modified initial condition using the first item and the last item of X (1) to enhance prediction accuracy of a traditional GM(1,1) model.…”

Section: Introductionmentioning

confidence: 99%

An Initial Condition Optimization Approach for Improving the Prediction Precision of a GM(1,1) Model

Madhi

Mohamed

2017

MCA

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Grey model GM(1,1) has attained excellent prediction accuracy with restricted data and has been broadly utilized in a range of areas. However, the GM(1,1) forecasting model sometimes yields large forecasting errors which directlyaffect the simulation and prediction precision directly. Therefore, the improvement of the GM(1,1) model is an essential issue, and the current study aims to enhance the prediction precision of the GM(1,1) model. Specifically, in order to improve the prediction precision of GM(1,1) model, it is necessary to consider improving the initial condition in the response function of the model. Consequently, the purpose of this paper is to put forward a new method to enhance the performance of the GM(1,1) model by optimizing its initial condition. The minimum sum of squared error was used to optimize the new initial condition of the model. The numerical outcomes show that the improved GM(1,1) model provides considerably better performance than traditional grey model GM(1,1) . The result demonstrates that the improved grey model GM(1,1) achieves the objective of minimizing the forecast errors.

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Discrete grey forecasting model and its optimization

Cited by 430 publications

References 6 publications

Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Grey Coupled Prediction Model for Traffic Flow with Panel Data Characteristics

An Initial Condition Optimization Approach for Improving the Prediction Precision of a GM(1,1) Model

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