An optimized nonlinear grey Bernoulli model and its applications

Lu, Jianshan; Xie, Weidong; Zhou, Hongbo; Zhang, Aijun

doi:10.1016/j.neucom.2015.11.032

Cited by 53 publications

(20 citation statements)

References 30 publications

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“…In addition, Wen et al [40] used the GM (1,1) model to predict air traffic flow and showed that the prediction capability of the GM (1,1) model is better than those of the ARIMA and multiple regression models through an experimental comparison. Guo et al [41] established a grey nonlinear delay GM (l, l) model to predict short-term traffic flow on urban roads, and Lu et al [42] used the nonlinear grey Bernoulli equation to obtain a grey prediction model for traffic flow prediction and achieved good results. Furthermore, Mao et al [43] constructed a grey triangular GM (l, l) model to predict traffic flow fluctuations, while Xiao et al [44] proposed a seasonal grey GM (1,1) rolling prediction model based on the cycle truncation accumulated generating operation (CTAGO).…”

Section: Introductionmentioning

confidence: 99%

A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors

Duan

Xiao

2019

Complexity

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Short-term traffic flow prediction is an important theoretical basis for intelligent transportation systems, and traffic flow data contain abundant multimode features and exhibit characteristic spatiotemporal correlations and dynamics. To predict the traffic flow state, it is necessary to design a model that can adapt to changing traffic flow characteristics. Thus, a dynamic tensor rolling nonhomogeneous discrete grey model (DTRNDGM) is proposed. This model achieves rolling prediction by introducing a cycle truncation accumulated generating operation; furthermore, the proposed model is unbiased, and it can perfectly fit nonhomogeneous exponential sequences. In addition, based on the multimode characteristics of traffic flow data tensors and the relationship between the cycle truncation accumulated generating operation and matrix perturbation to determine the cycle of dynamic prediction, the proposed model compensates for the periodic verification of the RSDGM and SGM grey prediction models. Finally, traffic flow data from the main route of Shaoshan Road, Changsha, Hunan, China, are used as an example. The experimental results show that the simulation and prediction results of DTRNDGM are good.

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Section: Introductionmentioning

confidence: 99%

A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors

Duan

Xiao

2019

Complexity

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“…In addition, it only needs four recent data points to achieve reliable and acceptable accuracy for future prediction (Wang and Hsu, 2008;. The GM(1,1) has been widely used in many fields such as management, economics and engineering, and the related models have been developed (Feng et al, 2012;Li et al, 2012;Pi et al, 2010;Lee and Tong, 2011;Mao and Chirwa, 2006;Hu et al, 2015;Hu, 2013;Tsaur and Liao, 2007;Chang et al, 2015Chang et al, , 2016Wang and Hao, 2016;Lu et al, 2016;Mao et al, 2016;Yuan et al, 2016). Moreover, grey prediction has been gaining in popularity in the past decade because of its simplicity and ability to characterize unknown systems by a few data points (Suganthi and Samuel, 2012).…”

Section: Introductionmentioning

confidence: 99%

Electricity consumption prediction using a neural-network-based grey forecasting approach

2017

Journal of the Operational Research Society

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Electricity consumption is an important economic index and plays a significant role in drawing up an energy development policy for each country. Multivariate techniques and time-series analysis have been proposed to deal with electricity consumption forecasting, but a large amount of historical data is required to obtain accurate predictions. The grey forecasting model attracted researchers by its ability to characterize an uncertain system effectively with a limited number of samples. GM(1,1) is the most frequently used grey forecasting model, but its developing coefficient and control variable were dependent on the background value that is not easy to be determined, whereas a neural-network-based GM(1,1) model called NNGM(1,1) has been presented to resolve this troublesome problem. This study has applied NNGM(1,1) to electricity consumption and has examined its forecasting ability on electricity consumption using sample data from the Turkish Ministry of Energy and Natural Resources and the Asia-Pacific Economic Cooperation energy database. Experimental results demonstrate that NNGM(1,1) performs well.

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“…The GM(1, 1) model needs only four recent sample data points to achieve reliable and acceptable prediction accuracy [ 16 , 17 ]. Its effectiveness has been verified through application to a wide range of real-world problems, including energy consumption forecasting [ 10 – 13 , 18 – 23 ], technology management [ 24 , 25 ], engineering problems [ 26 ], optimization model development [ 27 , 28 ], and general management [ 29 – 31 ].…”

Section: Introductionmentioning

confidence: 99%

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

2017

PLoS ONE

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Energy demand is an important economic index, and demand forecasting has played a significant role in drawing up energy development plans for cities or countries. As the use of large datasets and statistical assumptions is often impractical to forecast energy demand, the GM(1,1) model is commonly used because of its simplicity and ability to characterize an unknown system by using a limited number of data points to construct a time series model. This paper proposes a genetic-algorithm-based remnant GM(1,1) (GARGM(1,1)) with sign estimation to further improve the forecasting accuracy of the original GM(1,1) model. The distinctive feature of GARGM(1,1) is that it simultaneously optimizes the parameter specifications of the original and its residual models by using the GA. The results of experiments pertaining to a real case of energy demand in China showed that the proposed GARGM(1,1) outperforms other remnant GM(1,1) variants.

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An optimized nonlinear grey Bernoulli model and its applications

Cited by 53 publications

References 30 publications

A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors

A Multimode Dynamic Short-Term Traffic Flow Grey Prediction Model of High-Dimension Tensors

Electricity consumption prediction using a neural-network-based grey forecasting approach

A genetic-algorithm-based remnant grey prediction model for energy demand forecasting

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