International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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An Intelligent Gold Price Prediction Based on Automated Machine and k-fold Cross Validation Learning

  • Baguda, Yakubu S. (Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University) ;
  • Al-Jahdali, Hani Meateg (Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University)
  • Received : 2021.04.05
  • Published : 2021.04.30
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Abstract

The rapid change in gold price is an issue of concern in the global economy and financial markets. Gold has been used as a means for trading and transaction around the world for long period of time and it plays an integral role in monetary, business, commercial and financial activities. More importantly, it is used as economic measure for the global economy and will continue to play an important economic vital role - both locally and globally. There has been an explosive growth in demand for efficient and effective scheme to predict gold price due its volatility and fluctuation. Hence, there is need for the development of gold price prediction scheme to assist and support investors, marketers, and financial institutions in making effective economic and monetary decisions. This paper primarily proposed an intelligent based system for predicting and characterizing the gold market trend. The simulation result shows that the proposed intelligent gold price scheme has been able to predict the gold price with high accuracy and precision, and ultimately it has significantly reduced the prediction error when compared to baseline neural network (NN).

Keywords

Acknowledgement

The authors would like to thank all those who contributed toward making this research successful. Also, we would like to thanks the reviewers for their insightful and valuable comment. This work is supported by the Deanship of Scientific Research (DSR), King Abdulaziz University, Saudi Arabia, under grant D-64-830-1437. The authors are very grateful to the DSR for their technical and financial support throughout the period of the project.

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