Study on Price Forecasting for Gold Commodities Using Tree-Based Customized Adaboost Algorithm

Authors

  • Anandaraj SP Professor & HOD, Department of Computer Science and Engineering, Presidency University, Bangalore, India
  • Jyoti Shekhawat Assistant Professor, Department of Computer Science & Application, Vivekananda Global University, Jaipur, India
  • Hina Hashmi Assistant Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Apoorva Joshi Assistant Professor, Department of Master of Computer Application, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India

Keywords:

Gold commodity, price forecasting, tree-based customized AdaBoost (T-CA), accuracy

Abstract

For buyers and sellers to make wise choices, forecasting prices in the field of commodities is essential. The goal of this study is to create a reliable and accurate price forecasting model that is tailored to gold commodities. To do this, we suggest a brand-new tree-based AdaBoost (T-CA) technique that includes features specifically designed for the features of gold prices. The methodology of the report makes use of daily statistics from the World Gold Council between 1979 and 2019. We only utilize data from 2014 for model training and the rest for model validation. The outcomes show that our suggested algorithm performs better than the alternatives, with lower errors and higher forecasting abilities. The results of this study provide a trustworthy and effective method based on the T-CA for price forecasting gold commodities. The investors, sellers, and banks can make more precise predictions and improve how they invest in the gold sector thanks to the useful insights provided by the suggested T-CA model.

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Published

11.07.2023

How to Cite

SP, A. ., Shekhawat, J. ., Hashmi, H. ., & Joshi, A. . (2023). Study on Price Forecasting for Gold Commodities Using Tree-Based Customized Adaboost Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 429–433. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3070

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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