Study on Price Forecasting for Gold Commodities Using Tree-Based Customized Adaboost Algorithm - SP, Anandaraj; Shekhawat, Jyoti; Hashmi, Hina; Joshi, Apoorva (International Journal of Intelligent Systems and Applications in Engineering, 2023)
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.