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International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm

Author

Listed:
  • Wanbo Lu
  • Tingting Qiu
  • Wenhui Shi
  • Xiaojun Sun

Abstract

Considering the complexity pattern of the gold price, this paper adopts the decomposition‐reconstruction‐forecast‐mergence scheme to perform the international gold price forecast. The original gold price data are decomposed into 12 intrinsic mode functions and a residual by the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method, and then the 13 sequences are reconstructed into a high‐frequency subsequence (IMFH), a low‐frequency subsequence (IMFL), and the residual (Res). According to the different characteristics of the subsequences, the IMFL and Res are forecasted by the support vector regression (SVR) model. Besides, in order to further improve the prediction accuracy of IMFH, we have developed a novel hybrid method based on the support vector regression (SVR) model and the grey wolf optimizer (GWO) algorithm with SVR for predicting the IMFH of gold prices, i.e., the CEEMDAN‐GWO‐SVR model. This hybrid model combines the methodology of complex systems with machine learning techniques, making it more appropriate for analyzing relationships such as high‐frequency dependences and solving complex nonlinear problems. Finally, the final result is obtained by combining the forecasting results of the three subsequences. The empirical results show that the proposed model demonstrates the highest prediction ability among all of the investigated models in a comparison of prediction errors with other individual models.

Suggested Citation

  • Wanbo Lu & Tingting Qiu & Wenhui Shi & Xiaojun Sun, 2022. "International Gold Price Forecast Based on CEEMDAN and Support Vector Regression with Grey Wolf Algorithm," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:1511479
    DOI: 10.1155/2022/1511479
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    References listed on IDEAS

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    1. Parisi, Antonino & Parisi, Franco & Díaz, David, 2008. "Forecasting gold price changes: Rolling and recursive neural network models," Journal of Multinational Financial Management, Elsevier, vol. 18(5), pages 477-487, December.
    2. Jieying Gao & Huan Guo & Xin Xu & Sameh S. Askar, 2022. "Multifactor Stock Selection Strategy Based on Machine Learning: Evidence from China," Complexity, Hindawi, vol. 2022, pages 1-17, February.
    3. Taiyong Li & Zijie Qian & Ting He, 2020. "Short-Term Load Forecasting with Improved CEEMDAN and GWO-Based Multiple Kernel ELM," Complexity, Hindawi, vol. 2020, pages 1-20, February.
    4. Mauro Castelli & Fabiana Martins Clemente & Aleš Popovič & Sara Silva & Leonardo Vanneschi, 2020. "A Machine Learning Approach to Predict Air Quality in California," Complexity, Hindawi, vol. 2020, pages 1-23, August.
    5. Muhammad Ali & Dost Muhammad Khan & Muhammad Aamir & Amjad Ali & Zubair Ahmad & A. Dionisio, 2021. "Predicting the Direction Movement of Financial Time Series Using Artificial Neural Network and Support Vector Machine," Complexity, Hindawi, vol. 2021, pages 1-13, December.
    6. Jieying Gao & Huan Guo & Xin Xu, 2022. "Multifactor Stock Selection Strategy Based on Machine Learning: Evidence from China," Complexity, John Wiley & Sons, vol. 2022(1).
    7. Zhang, Pinyi & Ci, Bicong, 2020. "Deep belief network for gold price forecasting," Resources Policy, Elsevier, vol. 69(C).
    8. Weihe Wang & Weixuan Xia, 2017. "Empirical Modeling for the Spot Price of Gold Based on Influencing Factors," Applied Economics and Finance, Redfame publishing, vol. 4(3), pages 129-140, May.
    9. Alameer, Zakaria & Elaziz, Mohamed Abd & Ewees, Ahmed A. & Ye, Haiwang & Jianhua, Zhang, 2019. "Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm," Resources Policy, Elsevier, vol. 61(C), pages 250-260.
    10. Shafiee, Shahriar & Topal, Erkan, 2010. "An overview of global gold market and gold price forecasting," Resources Policy, Elsevier, vol. 35(3), pages 178-189, September.
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