Forecasting performance of grey prediction for education expenditure and school enrollment

Tang, Hui-Wen Vivian; Yin, Mu-Shang

doi:10.1016/j.econedurev.2011.12.007

Cited by 43 publications

(33 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GM (1,1) can be used when the size of input data is small for times series forecasting (Liu and Lin, 2010). The GM (1,1) has been used as a prediction tool in several different areas (Sum, 1991;Zhu and Cao, 1998;Wang, 2002;Mao and Chirwa, 2006;Chiang and Chen et al, 2008); Tang and Yin, 2012). Energy demand forecasting is another important usage area of grey prediction models (Yao et al, 2003;Zhou et al, 2006;Akay and Atak, 2007;Hsu and Chen, 2012;Chena and Wang, 2012;Lü and Lu, 2012;Chang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Primary energy sources planning based on demand forecasting: The case of Turkey

Hamzaçebi

2016

J. energy South. Afr.

View full text Add to dashboard Cite

show abstract

“…Step 9 Calculate MAPEM (%) and MPSVSC (%) with (21) and (23), analyze the simulative accuracy and apply the proposed model for forecasting.…”

Section: Steps Of Ig-ndgmmentioning

confidence: 99%

“…Others focused on developing new grey forecasting models for special developing rules, like S-curve and non-equidistance [13,14]. For solving real applications, the grey forecasting model has been widely applied in economic developing forecasting, energy forecasting, etc [15][16][17][18][19][20][21][22][23]. The results of these real applications showed that grey forecasting can catch the developing trend well in different cases.…”

Section: Introductionmentioning

confidence: 99%

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

Xie

Liu

2015

J. of Syst. Eng. Electron.

View full text Add to dashboard Cite

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 de ned. Based on the traditional nonhomogenous discrete grey forecasting model (NDGM), the interval grey number and its algebra operations are rede ned 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 de ned 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.

show abstract

“…It incorporates the usage of recent data to handle noisy sequence. Tang and Yin improved the forecasting accuracy for education expenditure by RM. Zhao et al , forecasted the per capita annual net income of rural households in China by using RM and obtained greater accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Liu

Wang

et al. 2014

Computational Intelligence

View full text Add to dashboard Cite

Grey system theory has been widely used to forecast the economic data that are often nonlinear, irregular, and nonstationary. Current forecasting models based on grey system theory could adapt to various economic time series data. However, these models ignored the importance of the model parameter optimization and the use of recent data, which lead to poor forecasting accuracy. In this article, we propose a novel forecasting model, called particle swarm optimization rolling grey model (PSO-RGM(1,1)), based on a rolling mechanism GM with optimized parameters by using the particle swarm optimization algorithm. The simple model is shown to be very effective in forecasting the tertiary industry data sequences, which are short and noisy but regular in secular trend. The experimental results show that PSO-RGM(1,1) outperforms other commonly used forecasting models on three real economic data sets. Our empirical study shows that PSO is found to be the best overall algorithm to optimize the parameter of RGM compared with other well-known metaheuristics. Furthermore, we evaluated other variant PSOs and found that single particle PSO outperforms others overall in terms of prediction accuracy, convergence speed, and degree of certainty.

show abstract

Forecasting performance of grey prediction for education expenditure and school enrollment

Cited by 43 publications

References 20 publications

Primary energy sources planning based on demand forecasting: The case of Turkey

Primary energy sources planning based on demand forecasting: The case of Turkey

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

A Rolling Grey Model Optimized by Particle Swarm Optimization in Economic Prediction

Contact Info

Product

Resources

About