Journal
NATURAL RESOURCES RESEARCH
Volume 28, Issue 4, Pages 1385-1401Publisher
SPRINGER
DOI: 10.1007/s11053-019-09473-w
Keywords
Adaptive neuro-fuzzy inference system (ANFIS); Genetic algorithm (GA); Support vector machine (SVM); Copper price forecasting; Computational intelligence forecasting
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Funding
- Chinese Guizhou Science and Technology Planning Project [20172803]
- Chinese Hubei Natural Science Foundation [2016CFB336]
- Chinese Scholarship Council (CSC)
- Egyptian Government
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An accurate forecasting model for the price volatility of minerals plays a vital role in future investments and decisions for mining projects and related companies. In this paper, a hybrid model is proposed to provide an accurate model for forecasting the volatility of copper prices. The proposed model combines the adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA). Genetic algorithms are used for estimating the ANFIS model parameters. The results of the proposed model are compared to other models, including ANFIS, support vector machine (SVM), generalized autoregressive conditional heteroscedasticity (GARCH), and autoregressive integrated moving average (ARIMA) models. The empirical results confirm the superiority of the hybrid GA-ANFIS model over other models. The proposed model also improves the forecasting accuracy obtained from the ANFIS, SVM, GARCH, and ARIMA models by a 62.92%, 36.38%, 91.72%, and 42.19% decrease in mean square error, respectively.
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