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A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting

Author

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  • E, Jianwei
  • Ye, Jimin
  • Jin, Haihong

Abstract

Gold, as a dominant ingredient in financial market, has gripped a large quantities of the financiers and scholars to research the formation mechanism of its price. Academic circles spring up plenty of methods to analyze and predict the gold price, such techniques are based on linear regression (MLR), support vector machine (SVM), artificial neural network (ANN), respectively. However, the existing methods cannot track the random and nonlinear features of the gold price well. The accurate and effective estimation models are acceptable for researching the temporal sequence, at the same time, it will be a powerful tool for governments and investors to formulate strategies.

Suggested Citation

  • E, Jianwei & Ye, Jimin & Jin, Haihong, 2019. "A novel hybrid model on the prediction of time series and its application for the gold price analysis and forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    • Handle: RePEc:eee:phsmap:v:527:y:2019:i:c:s037843711930843x
    DOI: 10.1016/j.physa.2019.121454
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    Citations

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    Cited by:

    1. Xiao, Yu-jie & Wang, Xiao-kang & Wang, Jian-qiang & Zhang, Hong-yu, 2021. "An adaptive decomposition and ensemble model for short-term air pollutant concentration forecast using ICEEMDAN-ICA," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    2. Mustafa Yurtsever, 2023. "Unemployment rate forecasting: LSTM-GRU hybrid approach," Journal for Labour Market Research, Springer;Institute for Employment Research/ Institut für Arbeitsmarkt- und Berufsforschung (IAB), vol. 57(1), pages 1-9, December.
    3. Fijorek, Kamil & Jurkowska, Aleksandra & Jonek-Kowalska, Izabela, 2021. "Financial contagion between the financial and the mining industries – Empirical evidence based on the symmetric and asymmetric CoVaR approach," Resources Policy, Elsevier, vol. 70(C).
    4. Liu, Qing & Liu, Min & Zhou, Hanlu & Yan, Feng, 2022. "A multi-model fusion based non-ferrous metal price forecasting," Resources Policy, Elsevier, vol. 77(C).
    5. Cai, Lingru & Zhang, Zhanchang & Yang, Junjie & Yu, Yidan & Zhou, Teng & Qin, Jing, 2019. "A noise-immune Kalman filter for short-term traffic flow forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    6. Nghia Chu & Binh Dao & Nga Pham & Huy Nguyen & Hien Tran, 2022. "Predicting Mutual Funds' Performance using Deep Learning and Ensemble Techniques," Papers 2209.09649, arXiv.org, revised Jul 2023.
    7. Cohen, Gil & Aiche, Avishay, 2023. "Forecasting gold price using machine learning methodologies," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    8. Wen Zhang & Zhibin Wu, 2022. "Optimal hybrid framework for carbon price forecasting using time series analysis and least squares support vector machine," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(3), pages 615-632, April.

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