A novel grey wave forecasting method for predicting metal prices

Chen, Yanhui; He, Kaijian; Zhang, Chuan

doi:10.1016/j.resourpol.2016.06.012

Cited by 51 publications

(15 citation statements)

References 24 publications

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“…However, because economic data fluctuates with upward and downward tendencies, an original Grey wave forecasting model is no longer suitable for this type of data series. Therefore, we choose to use the quantile of time series as a contour line to seize graphical information correctly proposed by Chen (2016) [36] (shown in Figure 1). …”

Section: Choosing Contour Linesmentioning

confidence: 99%

A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Yin

2017

Sustainability

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Abstract:The total trade volume of a country is an important way of appraising its international trade situation. A prediction based on trade volume will help enterprises arrange production efficiently and promote the sustainability of the international trade. Because the total Chinese trade volume fluctuates over time, this paper proposes a Grey wave forecasting model with a Hodrick-Prescott filter (HP filter) to forecast it. This novel model first parses time series into long-term trend and short-term cycle. Second, the model uses a general GM (1,1) to predict the trend term and the Grey wave forecasting model to predict the cycle term. Empirical analysis shows that the improved Grey wave prediction method provides a much more accurate forecast than the basic Grey wave prediction method, achieving better prediction results than autoregressive moving average model (ARMA).

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Section: Choosing Contour Linesmentioning

confidence: 99%

A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Yin

2017

Sustainability

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“…It requires only four recent sample data points to achieve reliable and acceptable prediction accuracy (Wen, 2004;Wang and Hsu, 2008), and has been widely applied to management, economics and engineering (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;Cui et al, 2013;Wei et al, 2015). To improve prediction accuracy of the original GM(1,1) model, several improved versions have been proposed, such as an improved grey model with convolution integral GMC(1, n) (Wang and Hao, 2016), a self-adaptive intelligence model (Zeng et al, 2016), Rolling-ALO-GM(1, 1) model for annual power load forecasting (Zhao and Guo, 2016), PGM(1,1) using particle swarm optimization to determine the development coefficient (Li et al, 2016) and grey wave forecasting that established GM(1,1) models on the basis of qualified contour time sequences (Chen et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Grey prediction with residual modification using functional-link net and its application to energy demand forecasting

2017

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Purpose Energy demand is an important economic index, and demand forecasting has a significant role when devising energy development plans for cities or countries. GM(1,1) model has become popular because it needs only a few data points to construct a time-series model without statistical assumptions. Several methods have been developed to improve prediction accuracy of the original GM(1,1) model by only estimating the sign of each residual. This study aims to address that this is too tight a restriction for the modification range. Design/methodology/approach Based on the predicted residual, this study uses the functional-link net (FLN) with genetic-algorithm-based learning to estimate the modification range for its corresponding predicted value obtained from the original GM(1,1) model. Findings The forecasting ability of the proposed grey prediction model is verified using real energy demand cases from China. Experimental results show that the proposed prediction model performs well compared to other grey residual modification models with sign estimation. Originality/value The proposed FLNGM(1,1) model can improve prediction accuracy of the original GM(1,1) model using residual modification. The distinctive feature of the proposed model is to use an FLN to estimate sign and modification range simultaneously for the predicted value based on its corresponding predicted residual obtained from the residual GM(1,1) model.

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“…It requires only four recent samples to derive reliable and acceptable prediction accuracy [5], and has been widely applied to various decision problems involving management, economics, and engineering [2][3][4][11][12][13][14][15][16]. To better improve the prediction performance of the original GM(1,1) model, several versions combining with computational intelligence have been proposed, such as models with self-adaptive intelligence [17], neural-network-based grey prediction for electricity consumption prediction [18,19], PGM(1,1) using particle swarm optimization to determine the development coefficient [20], GM(1,1) models with online sequential extreme learning machine [21], an optimized nonlinear grey Bernoulli model [22], an adaptive GM(1,1) for electricity consumption [3], and grey wave forecasting through qualified contour sequences [23]. Literally, the combination of grey prediction and neural networks can better represent system dynamics with uncertainty and nonlinearity [21].…”

Section: Introductionmentioning

confidence: 99%

A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

Jiang

Chiu

et al. 2017

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Grey prediction models for time series have been widely applied to demand forecasting because only limited data are required for them to build a time series model without any statistical assumptions. Previous studies have demonstrated that the combination of grey prediction with neural networks helps grey prediction perform better. Some methods have been presented to improve the prediction accuracy of the popular GM(1,1) model by using the Markov chain to estimate the residual needed to modify a predicted value. Compared to the previous Grey-Markov models, this study contributes to apply the functional-link net to estimate the degree to which a predicted value obtained from the GM(1,1) model can be adjusted. Furthermore, the troublesome number of states and their bounds that are not easily specified in Markov chain have been determined by a genetic algorithm. To verify prediction performance, the proposed grey prediction model was applied to an important grey system problem-foreign tourist forecasting. Experimental results show that the proposed model provides satisfactory results compared to the other Grey-Markov models considered.

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A novel grey wave forecasting method for predicting metal prices

Cited by 51 publications

References 24 publications

A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Grey prediction with residual modification using functional-link net and its application to energy demand forecasting

A Novel Grey Prediction Model Combining Markov Chain with Functional-Link Net and Its Application to Foreign Tourist Forecasting

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